Federated Learning Market by Application (Drug Discovery, Industrial IoT, Risk Management), Vertical (Healthcare and Life Sciences, BFSI, Manufacturing, Automotive and Transportation, Energy and Utilities) and Region - Global Forecast to 2028
[172 Pages Report] As per AS-IS scenario, the global federated learning market size to grow from USD 127 million in 2023 to USD 210 million by 2028, at a Compound Annual Growth Rate (CAGR) of 10.6% during the forecast period. The major growing factor of federated learning market is, federated learning allows numerous players to develop a shared, strong deep training models while sharing information, permitting crucial concerns such as data protection, confidentiality, information privileged access, and accessibility to large datasets to be addressed.
Market Dynamics
Driver: Growing adoption of federated learning in various applications for data privacy
The way ML methods are taught is changing due to federated learning. Companies are concentrating their efforts on doing an in-depth study on federated learning. Organizations can strengthen current algorithms and enhance their AI applications by using federated learning. The demand for more learning among gadgets as well as organizations is rising. In the wellness sector, federated learning might aid healthcare workers in providing good quality outcomes and accelerating medication development. For instance, a new peer-to-peer approach called FADNet is a solution to address centralized learning deficits. This approach does not need a centralized system for learning; instead, every client learns its information, as well as the outcome is sent to another user via a cycle of aggregating. They can obtain true traffic and carparks situations using active learning and federated learning, which are not always straightforward to confirm via modelling. This method decreases labeling time and cost while also improving the whole CI/CD workflow and installation of the self-driving vehicle machine learning algorithms. This method needs greater computer capacity, unlike federated learning which is centralized or server-based. The system may be trained with dispersed car or network sensors readings by integrating federation and learning approach. Regulations include federated learning spanning departments within just the firm, often drives federated learning within a business or firm. Laws, such as federated learning among departments of a firm, often drive federated learning within a business or firm. In some circumstances, though, rules may compel organizations to give their information. For instance, Google has introduced its first production-level federated learning platform, which will produce several federated learning-based applications, such as on-device item ranking, next-word prediction, and content suggestion.
Restraint: Lack of skilled technical expertise
The lack of skilled workers, specifically IT professionals, is a significant impediment which many firms face when adopting ML into existing workflows. Challenging for staff to grasp and implement federated learning systems as it is a revolutionary idea. Owing to a dearth of competent personnel to build and implement federated learning tasks which entail complicated methods such as machine learning, recruiting, and keeping technological capabilities became a significant concern for some businesses. As an organization, they need to build increasingly skillset and job titles. For instance, Engineers that can manage and comprehend the modern federated learning infrastructure associated with installation and maintenance machine learning algorithm are needed by organisations. Data scientists are most highly trained scientific specialists, having extensive understanding of computer science, statistics, and conceptual understanding. Qualified data scientists, on the other hand, demanding hefty fees and require successful things, which are often out of reach for SMEs or even major corporations. To stay relevant in a marketplace with limited ability, there is a growing demand of federated learning modules throughout sectors. As a result, the existing shortage of trained individuals is a key barrier for the worldwide federated learning solutions industry.
Opportunity: Federated learning enables distributed participants to collaboratively learn a commonly shared model while holding data locally
Federated learning is a method of training ML algorithms on decentralized data. Rather than storing data from a single computer or data mart, information is kept at original sources such as smartphones, manufacturing detecting gear, as well as other end devices, and the ML machines are trained on-the-fly. This helps make decisions before sending back to a centralized computer. For instance, this financial sector makes good use of federated learning for debt risk analysis. Typically, banks utilize whitelisting procedures to exclude consumers based on card details from federal reserve. By partnering with the other financial organizations and eCommerce enterprises, risk assessment variables, such as taxation and reputation, may be used. Because sharing personal customer data across firms is dangerous, firms may utilize federated learning to create a risk evaluation ML model. In another instance, when an eCommerce start-up seeks to create a model to assess its clients' likelihood to purchase a thing, it uses the data from its website or application to run simulations. Spending time on a manufacturer's website, items bought simultaneously, goods viewed but not bought, as well as other customer information are examples of this type of information. Each client has around 50 and 1,000 data points covering a period. This data is analyzed and transferred to a centralized data center or devices for processing. Information is vulnerable to hackers because of such a data transit. ML procedures are distributed using the federated learning method. It allows businesses to acquire a common paradigm in a cooperative way by sharing as well as storing data from the device.
Challenge: System integration and interoperability issue
Customers with varying computation and network control create system diversity. The sustainability of federated algorithms is harmed by such variability in personal computer rates, which leads to a considerable reduction in theoretical execution. Federated learning is using distant computers and walled storage systems to build predictive methods yet maintaining information locally, including cell devices or institutions. Education in heterogeneous and possibly huge networks posed new issues that necessitated a radical shift away from traditional techniques to huge ML, dispersed optimization, and data processing while maintaining privacy. Because of differences in technology internet connection (3G, 4G, 5G, and Wi-Fi) and energy, every item in federation systems may have different storing, computing, and communication networks. With the million-device network, the internet backbone capacity and framework restraints on every unit. This diversity that exists in multiple hardware requirements and variable conditions across participating devices is a major challenge in federated learning. In theory, heterogeneity can have a significant impact on the federated learning training phase, such as rendering a unit inaccessible for learning or preventing it from uploading model updates.
As per optimistic scenario, among verticals, the automotive and transportation segment to grow at a the highest CAGR during the forecast period
The federated learning solutions market is segmented on verticals into BFSI, healthcare and life sciences, retail and eCommerce, energy and utilities, and manufacturing, automotive and transportation, IT and telecommunication and other verticals (government, and media and entertainment). As per optimistic scenario, the automotive and transportation vertical is expected to grow at the highest CAGR during the forecast period. Autonomous vehicles have a complicated technology. Observation, forecasting, monitoring, localization, modelling, interfaces using public cloud, and data management are among the technology used in the system. With the introduction of automated vehicles, the focus was on data, edge-to-edge computer technology handling, and improved ML algorithm in addition to making automated vehicles reliable and secure for seamless integration through one area of the globe to another, even as analyzing information and personal confidentiality wirelessly. Effective learning chooses the most relevant pieces of data to classify and add to the instructional pool, which is expected to drive the growth of federated learning in the vertical.
As per optimistic scenario, among regions, Europe to hold the largest market size during the forecast period
As per optimistic scenario, Europe is estimated to account for the largest market size in the federated learning solutions market during the forecast period. The federated learning market for healthcare is categorized into various applications, such as patient data and risk analysis, medical imaging and diagnostics, precision medicine, lifestyle management and monitoring, drug discovery, inpatient care and hospital management, virtual assistant, wearables, and research. The drug discovery process is tedious, which requires the researcher to analyze vast quantities of bioscience information, including patents, genomic data, and a large number of publications uploaded daily across all biomedical journals and databases. This makes it necessary to evolve the drug discovery process, and federated learning has the capability to influence and enhance this process. Hence, to offer better platform across the market vendors in the market are developing new products. In Europe, the challenges linked to aging populations and shortages of healthcare professionals are driving up the adoption of AI technologies in healthcare. This is, in turn, driving the federated learning market growth in Europe.
Key Market Players
The federated learning solutions vendors have implemented various types of organic as well as inorganic growth strategies, such as new product launches, product upgradations, partnerships and agreements, business expansions, and mergers and acquisitions to strengthen their offerings in the market. The major vendors in the global federated learning solutions market include NVIDIA (US), Cloudera (US), IBM (US), Microsoft (US), Google (US), Intel(US), Owkin(US), Intellegens(UK), Edge Delta(US), Enveil(US), Lifebit(UK), DataFleets(US), Secure AI Labs(US), and Sherpa.AI(Spain).
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Report Metrics |
Details |
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Market size available for years |
2023–2028 |
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Base year considered |
2023 |
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Forecast period |
2023–2028 |
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Forecast units |
USD Thousands |
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Segments covered |
Application, Vertical and Region |
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Geographies covered |
North America, Europe, APAC, MEA, and Latin America |
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Companies covered |
NVIDIA (US), Cloudera (US), IBM (US), Microsoft (US), Google (US), Intel(US), Owkin(US), Intellegens(UK), Edge Delta(US), Enveil(US), Lifebit(UK), DataFleets(US), Secure AI Labs(US), Sherpa.AI(Spain), Decentralized Machine Learning(Singapore), Consilient(US), Apheris(Germany), Acuratio(US), FEDML(US). |
This research report categorizes the Federated Learning Market based on application, vertical, and region.
By Application:
- Drug Discovery
- Shopping Experience Personalization
- Data Privacy and Security Management
- Risk Management
- Industrial Internet of Things
- Online Visual Object Detection
- Agumented Reality/Virtual Reality
- Other Applications
By Verticals:
- Banking, Financial Services, and Insurance
- Healthcare and Life Sciences
- Retail and Ecommerce
- Manufacturing
- Energy and Utilities
- Automotive and Transportaion
- IT and Telecommunication
- Other Verticals
By Region:
- North America
- Europe
- APAC
- MEA
- Latin America
Recent Developments:
- In March 2022, NVIDIA launched Communications Intelligence Platform, a Clara Holoscan, solution which was designed for its healthcare sciences business has been updated to MGX, as a one-of-a-kind end-to-end system for both AI technologies and intelligent healthcare manufacturing and deployment in implantable augmentations.
- In January 2022, Intel launched OpenVINO integration with TensorFlow, the OpenVINO toolkit is used for online improvements and execution required for increased TensorFlow interoperability. It was created for programmers who want to try out the OpenVINO toolset and see how it might assist them boost the effectiveness of existing inferential apps with little code changes.
- In November 2021, NVIDIA launched NVIDIA FLARE, NVIDIA FLARE stands for Federated Learning Application Runtime Environment is an open-source platform, which is based on the foundation of NVIDIA Clara Train's federated learning software, and was employed for biomedical imagery, functional genomics, cancer, and COVID-19 research. Investigators and data scientists could use this SDK to convert their current ML techniques processes to a decentralized network. NVIDIA FLARE supports a variety of networked topologies, spanning peer-to-peer, asynchronous, and server-client techniques, among others.
- In June 2021, NVIDIA enhanced NVIDIA Clara, In NVIDIA Clara Train 4.0, a new tool named Homomorphic Encryption (HE) was added. It enables users to compute encrypted data. With Clair Train 4.0 the communication channel is established using SSL certificates and provisioning tool. For instance, NVIDIA used the TenSEAL module from OpenMined, a concise Python wrapper over Microsoft SEAL, to provide safe aggregating through federated learning by using HE.
- In February 2021, Enveil introduced new version of ZeroReveal 3.0. It delivers the homomorphic encryption-powered capabilities through an efficient and decentralized framework designed to reduce risk and address business challenges, including data sharing, collaboration, monetization, and regulatory compliance. The solution enhancements delivered in 3.0 releases strengthen integration, performance, and user experience features for both Enveil’s ZeroReveal Search and ZeroReveal Machine Learning product lines.
Frequently Asked Questions (FAQ):
What is federated learning?
According to Cloudera, federated learning is a paradigm in which Machine Learning (ML) models are trained on decentralized data. Instead of collecting data on a single server or data lake, it remains in place, on smartphones, industrial sensing equipment, and other edge devices, and the models are trained on-device. The trained models are transferred to a central server and combined. Transporting models rather than data has numerous ramifications and tradeoffs.
According to IBM, federated learning is a distributed ML process in which different parties collaborate to jointly train a ML model without the need to share training data with the other parties.
According to Owkin, federated learning is a ML procedure where the goal is to train a high-quality model with data distributed over several independent providers. Instead of gathering the data on a single central server, the data remains locked on their server, and the algorithms and predictive models travel between them.
Which regions are early adopter of federated learning solutions?
Europe and North America are at the initial stage towards adoption of federated learning solutions.
Which are key verticals adopting federated learning solutions?
Key verticals adopting federated learning solutions include healthcare and life sciences, BFSI, retail and eCommerce, manufacturing, automotive and transportation, IT and telecommunication and energy and utilities.
Which are the key vendors exploring federated learning space?
The key vendors exploring federated learning include NVIDIA, IBM, Cloudera, Microsoft, Google, Owkin, Intellegens, Secure AI Labs, Lifebit, Edge Delta, and many others offering federated learning solutions or focused towards various research project initiative.
What are the key applications of federated learning?
Drug discovery, data privacy and security management, risk management, shopping experience personalization, industrial IoT, online visual object detection, Agumented Reality/Virtual Reality and other applications such as video analytics, corporate IT, genomics, and anomaly detection may be the key applications end-users will look for federated learning. .
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TABLE OF CONTENTS
1 INTRODUCTION (Page No. - 21)
1.1 OBJECTIVES OF THE STUDY
1.2 MARKET DEFINITION
1.2.1 INCLUSIONS AND EXCLUSIONS
1.3 MARKET SCOPE
1.3.1 MARKET SEGMENTATION
1.3.2 YEARS CONSIDERED FOR THE STUDY
1.4 CURRENCY CONSIDERED
TABLE 1 UNITED STATES DOLLAR EXCHANGE RATE, 2018–2021
1.5 STAKEHOLDERS
1.6 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY (Page No. - 25)
FIGURE 1 FEDERATED LEARNING MARKET: RESEARCH DESIGN
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
TABLE 2 PRIMARY INTERVIEWS
2.1.2.1 Breakup of primary profiles
2.1.2.2 Key industry insights
2.2 MARKET BREAKUP AND DATA TRIANGULATION
FIGURE 2 DATA TRIANGULATION
2.3 MARKET SIZE ESTIMATION
FIGURE 3 MARKET: MARKET ESTIMATION APPROACH
2.4 MARKET FORECAST
TABLE 3 CRITICAL FACTORS IMPACTING THE MARKET GROWTH
2.5 ASSUMPTIONS FOR THE STUDY
2.6 LIMITATIONS OF THE STUDY
3 EXECUTIVE SUMMARY (Page No. - 35)
3.1 FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 4 GLOBAL FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSAND)
FIGURE 5 HEALTHCARE AND LIFE SCIENCES VERTICAL TO HOLD THE LARGEST MARKET SHARE DURING THE FORECAST PERIOD
FIGURE 6 EUROPE TO HOLD THE LARGEST MARKET SHARE BY 2023
3.2 SUMMARY OF KEY FINDINGS
4 MARKET OVERVIEW AND INDUSTRY TRENDS (Page No. - 40)
4.1 INTRODUCTION
4.2 FEDERATED LEARNING: EVOLUTION
FIGURE 7 EVOLUTION OF THE MARKET
4.3 FEDERATED LEARNING: TYPES
FIGURE 8 TYPES OF FEDERATED LEARNING
4.4 FEDERATED LEARNING: ARCHITECTURE
FIGURE 9 ARCHITECTURE OF FEDERATED LEARNING
4.5 MARKET DYNAMICS
FIGURE 10 DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES: FEDERATED LEARNING MARKET
4.5.1 DRIVERS
4.5.1.1 Growing need to increase learning between devices and organizations
4.5.1.2 Ability to ensure better data privacy and security by training algorithms on decentralized devices
4.5.1.3 Growing adoption of federated learning in various applications for data privacy
4.5.1.4 Ability of federated learning to address the difficulty of safeguarding individuals’ anonymity
4.5.2 RESTRAINTS
4.5.2.1 Lack of skilled technical expertise
4.5.3 OPPORTUNITIES
4.5.3.1 Federated learning enables distributed participants to collaboratively learn a commonly shared model while holding data locally
4.5.3.2 Capability to enable predictive features on smart devices without impacting the user experience and leaking private information
4.5.4 CHALLENGES
4.5.4.1 Issues of high latency and communication inefficiency
4.5.4.2 System integration and interoperability issue
4.5.4.3 Indirect information leakage
4.6 IMPACT OF DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES ON THE FEDERATED LEARNING MARKET
4.7 ARTIFICIAL INTELLIGENCE: ECOSYSTEM
FIGURE 11 ARTIFICIAL INTELLIGENCE ECOSYSTEM
4.8 USE CASE ANALYSIS
4.8.1 WEBANK AND A CAR RENTAL SERVICE PROVIDER ENABLE INSURANCE INDUSTRY TO REDUCE DATA TRAFFIC VIOLATIONS THROUGH FEDERATED LEARNING
4.8.2 FEDERATED LEARNING ENABLE HEALTHCARE COMPANIES TO ENCRYPT AND PROTECT PATIENT’S DATA
4.8.3 WEBANK AND EXTREME VISION INTRODUCED ONLINE VISUAL OBJECT DETECTION PLATFORM POWERED BY FEDERATED LEARNING TO STORE DATA IN CLOUD
4.8.4 WEBANK INTRODUCED FEDERATED LEARNING MODEL FOR ANTI-MONEY LAUNDERING
4.8.5 INTELLEGENS SOLUTION ADOPTION MAY HELP CLINICALS ANALYZE HEART RATE DATA
4.9 SUPPLY CHAIN ANALYSIS
FIGURE 12 SUPPLY CHAIN ANALYSIS
4.10 PATENT ANALYSIS
4.10.1 METHODOLOGY
4.10.2 DOCUMENT TYPE
TABLE 4 PATENTS FILED
4.10.3 INNOVATION AND PATENT APPLICATIONS
FIGURE 13 TOTAL NUMBER OF PATENTS GRANTED IN A YEAR, 2015–2021
4.10.3.1 Top applicants
FIGURE 14 TOP TEN COMPANIES WITH THE HIGHEST NUMBER OF PATENT APPLICATIONS, 2015–2021
TABLE 5 TOP EIGHT PATENT OWNERS (US) IN THE FEDERATED LEARNING MARKET, 2015–2021
4.11 TECHNOLOGY ANALYSIS
4.11.1 FEDERATED LEARNING VS DISTRIBUTED MACHINE LEARNING
4.11.2 FEDERATED LEARNING VS EDGE COMPUTING
4.11.3 FEDERATED LEARNING VS FEDERATED DATABASE SYSTEMS
4.11.4 FEDERATED LEARNING VS SWARM LEARNING
4.12 RESEARCH PROJECTS: FEDERATED LEARNING
4.12.1 MACHINE LEARNING LEDGER ORCHESTRATION FOR DRUG DISCOVERY (MELLODDY)
4.12.1.1 Participants
4.12.2 FEDAI
4.12.3 PADDLEPADDLE
4.12.4 FEATURECLOUD
4.12.5 MUSKETEER PROJECT
4.13 REGULATORY LANDSCAPE
4.13.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 6 NORTH AMERICA: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 7 EUROPE: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 8 ASIA PACIFIC: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 9 REST OF WORLD: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
4.13.2 REGULATORY IMPLICATIONS AND INDUSTRY STANDARDS
4.13.3 GENERAL DATA PROTECTION REGULATION
4.13.4 SEC RULE 17A-4
4.13.5 ISO/IEC 27001
4.13.6 SYSTEM AND ORGANIZATION CONTROLS 2 TYPE II COMPLIANCE
4.13.7 FINANCIAL INDUSTRY REGULATORY AUTHORITY
4.13.8 FREEDOM OF INFORMATION ACT
4.13.9 HEALTH INSURANCE PORTABILITY AND ACCOUNTABILITY ACT PLAY
4.14 KEY CONFERENCES AND EVENTS IN 2022
TABLE 10 FEDERATED LEARNING MARKET: DETAILED LIST OF CONFERENCES AND EVENTS
4.15 KEY STAKEHOLDERS AND BUYING CRITERIA
4.15.1 KEY STAKEHOLDERS IN THE BUYING PROCESS
FIGURE 15 INFLUENCE OF STAKEHOLDERS IN THE BUYING PROCESS FOR TOP VERTICALS
TABLE 11 INFLUENCE OF STAKEHOLDERS IN THE BUYING PROCESS FOR TOP VERTICALS (%)
TABLE 12 BUYING PROCESS FOR TOP VERTICALS
4.15.2 BUYING CRITERIA
FIGURE 16 KEY BUYING CRITERIA FOR TOP THREE VERTICALS
TABLE 13 KEY BUYING CRITERIA FOR TOP THREE VERTICALS
4.16 TRENDS/DISRUPTIONS IMPACTING BUYERS
FIGURE 17 MARKET: TRENDS/DISRUPTIONS IMPACTING BUYERS
5 FEDERATED LEARNING MARKET, BY APPLICATION (Page No. - 75)
5.1 INTRODUCTION
5.2 DRUG DISCOVERY
5.2.1 ABILITY TO ACCELERATE DRUG DISCOVERY BY ENABLING INCREASED COLLABORATIONS FOR FASTER TREATMENT TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
5.2.2 ASSURANCE OF DATA PRIVACY IS CREATING OPPORTUNITIES FOR FEDERATED LEARNING
5.3 SHOPPING EXPERIENCE PERSONALIZATION
5.3.1 GROWING FOCUS ON ENABLING PERSONALIZED SHOPPING EXPERIENCE WHILE ENSURING CUSTOMER DATA PRIVACY AND NETWORK TRAFFIC REDUCTION TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
5.3.2 USE OF FEDERATED LEARNING IN PERSONALIZED RECOMMENDATION
5.4 DATA PRIVACY AND SECURITY MANAGEMENT
5.4.1 FEDERATED LEARNING SOLUTIONS ENABLE BETTER DATA PRIVACY AND SECURITY MANAGEMENT BY LIMITING THE NEED TO MOVE DATA ACROSS NETWORKS BY TRAINING ALGORITHM
5.4.2 FEDERATED LEARNING HAS EMERGED AS A SOLUTION FOR FACILITATING REMOTE GROUP WORK WHILE KEEPING THE LEARNING DATA PRIVATE
5.5 RISK MANAGEMENT
5.5.1 ABILITY TO ENABLE BFSI ORGANIZATIONS TO COLLABORATE AND LEARN A SHARED PREDICTION MODEL WITHOUT SHARING DATA AND PERFORM EFFICIENT CREDIT RISK ASSESSMENT TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
5.5.2 FEDERATED MACHINE LEARNING FOR LOAN RISK PREDICTION
5.6 INDUSTRIAL INTERNET OF THINGS
5.6.1 FEDERATED LEARNING SOLUTIONS ENABLE PREDICTIVE MAINTENANCE ON EDGE DEVICES WITHOUT CENTRALIZING DATA
5.6.2 BLOCKCHAIN BASED FEDERATED LEARNING SOLUTIONS HELPS IN DEVICE RECOGNITION IN IIOT
5.7 ONLINE VISUAL OBJECT DETECTION
5.7.1 ABILITY TO ENABLE SAFETY MONITORING BY ENHANCED ONLINE VISUAL OBJECT DETECTION FOR SMART CITY APPLICATIONS TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
5.7.2 FEDCV A FRAMEWORK FOR DIVERSE COMPUTER VISION TASKS
5.8 AUGMENTED REALITY/VIRTUAL REALITY
5.8.1 OUTPUT SECURITY FOR MULTI-USER AUGMENTED REALITY USING FEDERATED REINFORCEMENT LEARNING
5.9 OTHER APPLICATIONS
6 FEDERATED LEARNING MARKET, BY VERTICAL (Page No. - 84)
6.1 INTRODUCTION
TABLE 14 PESSIMISTIC SCENARIO: MARKET SIZE, BY VERTICAL, 2023–2028 (USD THOUSANDS)
TABLE 15 AS-IS SCENARIO: MARKET SIZE, BY VERTICAL, 2023–2028 (USD THOUSANDS)
TABLE 16 OPTIMISTIC SCENARIO: MARKET SIZE, BY VERTICAL, 2023–2028 (USD THOUSANDS)
6.2 BANKING, FINANCIAL SERVICES, AND INSURANCE
6.2.1 ABILITY TO REDUCE MALICIOUS ACTIVITIES AND PROTECT CUSTOMER DATA TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS IN THE BFSI VERTICAL
6.2.2 BANKING, FINANCIAL SERVICES, AND INSURANCE: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 18 BANKING, FINANCIAL SERVICES, AND INSURANCE: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
6.3 HEALTHCARE AND LIFE SCIENCES
6.3.1 LARGE POOL OF APPLICATIONS, MULTIPLE RESEARCH INITIATIVES, AND COLLABORATIONS AMONG TECHNOLOGY VENDORS AND HEALTHCARE AND LIFE SCIENCES ORGANIZATIONS TO DRIVE MARKET GROWTH
6.3.2 HEALTHCARE AND LIFE SCIENCES: FORECAST 2023–2028 (OPTIMISTIC/ AS-IS/PESSIMISTIC)
FIGURE 19 HEALTHCARE AND LIFE SCIENCES: MARKET, 2023–2028 (USD THOUSANDS)
6.4 RETAIL AND ECOMMERCE
6.4.1 ABILITY TO ENABLE PERSONALIZED CUSTOMER EXPERIENCES WHILE ENSURING CUSTOMER DATA PRIVACY TO DRIVE THE ADOPTION OF FEDERATED LEARNING IN THE RETAIL AND ECOMMERCE VERTICAL
6.4.2 RETAIL AND ECOMMERCE: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 20 RETAIL AND ECOMMERCE: THE FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
6.5 MANUFACTURING
6.5.1 FOCUS ON SMART MANUFACTURING AND NEED FOR ENHANCED OPERATIONAL INTELLIGENCE TO DRIVE THE ADOPTION OF FEDERATED LEARNING ACROSS THE MANUFACTURING VERTICAL
6.5.2 MANUFACTURING: FORECAST 2023–2028 (OPTIMISTIC/ AS-IS/PESSIMISTIC)
FIGURE 21 MANUFACTURING: THE MARKET, 2023–2028 (USD THOUSANDS)
6.6 ENERGY AND UTILITIES
6.6.1 NEED TO CONTROL CYBERATTACKS AND IMPROVE POWER GRID RESILIENCE TO DRIVE THE ADOPTION OF FEDERATED LEARNING IN THE ENERGY AND UTILITIES VERTICAL
6.6.2 ENERGY AND UTILITIES: FORECAST 2023–2028 (OPTIMISTIC/ AS-IS/PESSIMISTIC)
FIGURE 22 ENERGY AND UTILITIES: THE FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
6.7 AUTOMOTIVE AND TRANSPORTATION
6.7.1 FEDERATED LEARNING TO RETRAIN THE NETWORK ACROSS NUMEROUS DEVICES IN A DECENTRALIZED MANNER
6.7.2 AUTOMOTIVE AND TRANSPORTATION: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 23 AUTOMOTIVE AND TRANSPORTATION: THE MARKET, 2023–2028 (USD THOUSANDS)
6.8 IT AND TELECOMMUNICATION
6.8.1 TRANSFER OF DATA RAISES PRIVACY CONCERNS CAUSING SAFETY AND ECONOMIC DIFFICULTIES
6.8.2 IT AND TELECOMMUNICATION: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 24 IT AND TELECOMMUNICATION: THE MARKET, 2023–2028 (USD THOUSANDS)
6.9 OTHER VERTICALS
FIGURE 25 OTHER VERTICALS: THE MARKET, 2023–2028 (USD THOUSANDS)
7 FEDERATED LEARNING MARKET, BY REGION (Page No. - 99)
7.1 INTRODUCTION
TABLE 17 PESSIMISTIC SCENARIO: MARKET SIZE, BY REGION, 2023–2028 (USD THOUSANDS)
TABLE 18 AS-IS SCENARIO: MARKET SIZE, BY REGION, 2023–2028 (USD THOUSANDS)
TABLE 19 OPTIMISTIC SCENARIO: MARKET SIZE, BY REGION, 2023–2028 (USD THOUSANDS)
7.2 NORTH AMERICA
7.2.1 HIGH FOCUS OF NORTH AMERICAN COMPANIES TOWARD RESEARCH IN FEDERATED LEARNING TO ENABLE FUTURISTIC DATA-TRAINED MODELS
7.2.2 NORTH AMERICA: MARKET DRIVERS
7.2.3 NORTH AMERICA: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 26 NORTH AMERICA: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
7.2.4 NORTH AMERICA: REGULATIONS
7.2.4.1 Health Insurance Portability and Accountability Act of 1996
7.2.4.2 California Consumer Privacy Act
7.2.4.3 Gramm–Leach–Bliley Act
7.2.4.4 Health Information Technology for Economic and Clinical Health Act
7.2.4.5 Federal Information Security Management Act
7.2.4.6 Payment Card Industry Data Security Standard
7.2.4.7 Federal Information Processing Standards
7.2.4.8 Sarbanes Oxley Act
7.2.4.9 United States Securities and Exchange Commission
7.3 EUROPE
7.3.1 HIGH FOCUS ON DATA PRIVACY AND COMPLIANCE, AND INCREASED RESEARCH COLLABORATIONS TO DRIVE THE ADOPTION OF FEDERATED LEARNING IN EUROPE
7.3.2 EUROPE: FEDERATED LEARNING MARKET DRIVERS
7.3.3 EUROPE: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 27 EUROPE: MARKET, 2023–2028 (USD THOUSANDS)
7.3.4 EUROPE: REGULATIONS
7.3.4.1 General Data Protection Regulation
7.3.4.2 European Committee for Standardization
7.3.4.3 European Technical Standards Institute
7.3.4.4 European Market Infrastructure Regulation
7.4 ASIA PACIFIC
7.4.1 COUNTRY-WISE FOCUS ON DATA PRIVACY REGULATIONS ALONG WITH THE INCREASING ADOPTION OF EDGE AI AND THE NEED FOR PERSONALIZED SERVICES TO SPUR THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
7.4.2 ASIA PACIFIC: MARKET DRIVERS
7.4.3 ASIA PACIFIC: FORECAST 2023–2028 (OPTIMISTIC/AS-IS /PESSIMISTIC)
FIGURE 28 ASIA PACIFIC: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
7.4.4 ASIA PACIFIC: REGULATIONS
7.4.4.1 Privacy Commissioner for Personal Data
7.4.4.2 Act on the Protection of Personal Information
7.4.4.3 Critical information infrastructure
7.4.4.4 International organization for standardization 27001
7.4.4.5 Personal data protection act
7.5 MIDDLE EAST AND AFRICA
7.5.1 STRENGTHENING OF NETWORK INFRASTRUCTURE, GROWING FOOTHOLD OF GLOBAL COMPANIES, AND INCREASING TECHNOLOGY ADOPTION TO DRIVE THE ADOPTION OF FEDERATED LEARNING
7.5.2 MIDDLE EAST AND AFRICA: MARKET DRIVERS
7.5.3 MIDDLE EAST AND AFRICA: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 29 MIDDLE EAST AND AFRICA: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
7.5.4 MIDDLE EAST AND AFRICA: REGULATIONS
7.5.4.1 Israeli Privacy Protection Regulations (Data Security), 5777-2017
7.5.4.2 Cloud Computing Framework
7.5.4.3 GDPR applicability in the Kingdom of Saudi Arabia
7.5.4.4 Protection of Personal Information Act
7.6 LATIN AMERICA
7.6.1 GROWING ADOPTION OF AI TECHNOLOGY TO DRIVE THE FEDERATED LEARNING MARKET
7.6.2 LATIN AMERICA: MARKET DRIVERS
7.6.3 LATIN AMERICA: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/ PESSIMISTIC)
FIGURE 30 LATIN AMERICA: MARKET, 2023–2028 (USD THOUSANDS)
7.6.4 LATIN AMERICA: REGULATIONS
7.6.4.1 Brazil Data Protection Law
7.6.4.2 Argentina Personal Data Protection Law No. 25.326
7.6.4.3 Federal Law on Protection of Personal Data Held by Individuals
8 COMPETITIVE LANDSCAPE (Page No. - 117)
8.1 INTRODUCTION
FIGURE 31 MARKET EVALUATION FRAMEWORK
8.2 KEY PLAYER STRATEGIES/RIGHT TO WIN
8.2.1 OVERVIEW OF STRATEGIES ADOPTED BY KEY FEDERATED LEARNING VENDORS
8.3 HISTORICAL REVENUE ANALYSIS OF TOP VENDORS
FIGURE 32 HISTORICAL REVENUE ANALYSIS
8.4 COMPETITIVE BENCHMARKING
TABLE 20 MARKET: NEW LAUNCHES, 2019–2022
TABLE 21 FEDERATED LEARNING MARKET: DEALS, 2019–2022
9 COMPANY PROFILES (Page No. - 126)
(Business Overview, Products Offered, Recent Developments, MnM View Right to win, Strategic choices made, Weaknesses and competitive threats) *
9.1 INTRODUCTION
9.2 KEY PLAYERS
9.2.1 NVIDIA
TABLE 22 NVIDIA: BUSINESS OVERVIEW
FIGURE 33 NVIDIA: COMPANY SNAPSHOT
TABLE 23 NVIDIA: SOLUTIONS OFFERED
TABLE 24 NVIDIA: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 25 NVIDIA: DEALS
FIGURE 34 BUSINESS MODEL CANVAS: NVIDIA
9.2.2 GOOGLE
TABLE 26 GOOGLE: BUSINESS OVERVIEW
FIGURE 35 GOOGLE: COMPANY SNAPSHOT
TABLE 27 GOOGLE: SOLUTIONS OFFERED
TABLE 28 GOOGLE: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 29 GOOGLE: OTHERS
FIGURE 36 BUSINESS MODEL CANVAS: GOOGLE
9.2.3 MICROSOFT
TABLE 30 MICROSOFT: BUSINESS OVERVIEW
FIGURE 37 MICROSOFT: COMPANY SNAPSHOT
TABLE 31 MICROSOFT: SOLUTIONS OFFERED
TABLE 32 MICROSOFT: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 33 MICROSOFT: DEALS
TABLE 34 MICROSOFT: OTHERS
FIGURE 38 BUSINESS MODEL CANVAS: MICROSOFT
9.2.4 IBM
TABLE 35 IBM: BUSINESS OVERVIEW
FIGURE 39 IBM: COMPANY SNAPSHOT
TABLE 36 IBM: SOLUTIONS OFFERED
TABLE 37 IBM: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 38 IBM: DEALS
FIGURE 40 BUSINESS MODEL CANVAS: IBM
9.2.5 CLOUDERA
TABLE 39 CLOUDERA: BUSINESS OVERVIEW
FIGURE 41 CLOUDERA: COMPANY SNAPSHOT
TABLE 40 CLOUDERA: SOLUTIONS OFFERED
TABLE 41 CLOUDERA: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 42 CLOUDERA: DEALS
FIGURE 42 BUSINESS MODEL CANVAS: CLOUDERA
9.2.6 INTEL
TABLE 43 INTEL: BUSINESS OVERVIEW
FIGURE 43 INTEL: COMPANY SNAPSHOT
TABLE 44 INTEL: SOLUTIONS OFFERED
TABLE 45 INTEL: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 46 INTEL: DEALS
TABLE 47 INTEL: OTHERS
9.2.7 OWKIN
TABLE 48 OWKIN: BUSINESS OVERVIEW
TABLE 49 OWKIN: SOLUTIONS OFFERED
TABLE 50 OWKIN: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 51 OWKIN: DEALS
TABLE 52 OWKIN: OTHERS
9.2.8 INTELLEGENS
TABLE 53 INTELLEGENS: BUSINESS OVERVIEW
TABLE 54 INTELLEGENS: SOLUTIONS OFFERED
TABLE 55 INTELLEGENS: DEALS
TABLE 56 INELLEGENS: OTHERS
9.2.9 EDGE DELTA
TABLE 57 EDGE DELTA: BUSINESS OVERVIEW
TABLE 58 EDGE DELTA: SOLUTIONS OFFERED
TABLE 59 EDGE DELTA: DEALS
TABLE 60 EDGE DELTA: OTHERS
9.2.10 ENVEIL
TABLE 61 ENVEIL: BUSINESS OVERVIEW
TABLE 62 ENVEIL: SOLUTIONS OFFERED
TABLE 63 ENVEIL: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 64 ENVEIL: OTHERS
9.2.11 LIFEBIT
TABLE 65 LIFEBIT: BUSINESS OVERVIEW
TABLE 66 LIFEBIT: SOLUTIONS OFFERED
TABLE 67 LIFEBIT: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 68 LIFEBIT: DEALS
TABLE 69 LIFEBIT: OTHERS
9.2.12 DATAFLEETS
TABLE 70 DATAFLEETS: BUSINESS OVERVIEW
TABLE 71 DATAFLEETS: SOLUTIONS OFFERED
TABLE 72 DATAFLEETS: DEALS
TABLE 73 DATAFLEETS: OTHERS
9.3 OTHERS KEY PLAYERS
9.3.1 SECURE AI LABS
9.3.2 SHERPA.AI
9.3.3 DECENTRALIZED MACHINE LEARNING
9.3.4 CONSILIENT
9.3.5 APHERIS
9.3.6 ACURATIO
9.3.7 FEDML
*Details on Business Overview, Products Offered, Recent Developments, MnM View, Right to win, Strategic choices made, Weaknesses and competitive threats might not be captured in case of unlisted companies.
10 ADJACENT AND RELATED MARKETS (Page No. - 175)
10.1 INTRODUCTION
10.1.1 RELATED MARKETS
10.1.2 LIMITATIONS
10.2 ARTIFICIAL INTELLIGENCE MARKET – GLOBAL FORECAST TO 2026
10.2.1 MARKET DEFINITION
10.2.2 MARKET OVERVIEW
TABLE 74 ARTIFICIAL INTELLIGENCE MARKET SIZE AND GROWTH RATE, 2021–2026 (USD BILLION, Y-O-Y%)
10.2.2.1 Artificial intelligence market, by vertical
TABLE 75 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY VERTICAL, 2021–2026 (USD BILLION)
10.2.2.2 Artificial intelligence market, by deployment mode
TABLE 76 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY DEPLOYMENT MODE, 2021–2026 (USD BILLION)
10.2.2.3 Machine learning market, by organization size
TABLE 77 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY ORGANIZATION SIZE, 2021–2026 (USD BILLION)
10.2.2.4 Artificial intelligence market, by service
TABLE 78 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY SERVICE, 2021–2026 (USD BILLION)
10.2.2.5 Artificial intelligence market, by region
TABLE 79 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY REGION, 2021–2026 (USD BILLION)
10.3 MACHINE LEARNING MARKET - GLOBAL FORECAST TO 2022
10.3.1 MARKET DEFINITION
10.3.2 MARKET OVERVIEW
TABLE 80 GLOBAL MACHINE LEARNING MARKET SIZE AND GROWTH RATE, 2015–2022 (USD MILLION, Y-O-Y %)
10.3.2.1 Machine learning market, by vertical
TABLE 81 MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
10.3.2.2 Machine learning market, by deployment mode
TABLE 82 MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
10.3.2.3 Machine learning market, by organization size
TABLE 83 MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
10.3.2.4 Machine learning market, by service
TABLE 84 MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
10.3.2.5 Machine learning market, by region
TABLE 85 MACHINE LEARNING MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
10.4 EDGE AI SOFTWARE MARKET - GLOBAL FORECAST TO 2026
10.4.1 MARKET DEFINITION
10.4.2 MARKET OVERVIEW
TABLE 86 GLOBAL EDGE AI SOFTWARE MARKET SIZE AND GROWTH RATE, 2014–2019 (USD MILLION, Y-O-Y%)
TABLE 87 GLOBAL EDGE AI SOFTWARE MARKET SIZE AND GROWTH RATE, 2019–2026 (USD MILLION, Y-O-Y%)
10.4.2.1 Edge AI software market, by component
TABLE 88 EDGE AI SOFTWARE MARKET SIZE, BY COMPONENT, 2014–2019 (USD MILLION)
TABLE 89 EDGE AI SOFTWARE MARKET SIZE, BY COMPONENT, 2019–2026 (USD MILLION)
10.4.2.2 Edge AI software market, by data source
TABLE 90 EDGE AI SOFTWARE MARKET SIZE, BY DATA SOURCE, 2014–2019 (USD MILLION)
TABLE 91 EDGE AI SOFTWARE MARKET SIZE, BY DATA SOURCE, 2019–2026 (USD MILLION)
10.4.2.3 Edge AI software market, by application
TABLE 92 EDGE AI SOFTWARE MARKET SIZE, BY APPLICATION, 2014–2019 (USD MILLION)
TABLE 93 EDGE AI SOFTWARE MARKET SIZE, BY APPLICATION, 2019–2026 (USD MILLION)
10.4.2.4 Edge AI software market, by vertical
TABLE 94 EDGE AI SOFTWARE MARKET SIZE, BY VERTICAL, 2014–2019 (USD MILLION)
TABLE 95 EDGE AI SOFTWARE MARKET SIZE, BY VERTICAL, 2019–2026 (USD MILLION)
10.4.2.5 Edge AI software market, by region
TABLE 96 EDGE AI SOFTWARE MARKET SIZE, BY REGION, 2014–2019 (USD MILLION)
TABLE 97 EDGE AI SOFTWARE MARKET SIZE, BY REGION, 2019–2026 (USD MILLION)
11 APPENDIX (Page No. - 189)
11.1 DISCUSSION GUIDE
11.2 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
11.3 AVAILABLE CUSTOMIZATIONS
11.4 RELATED REPORTS
11.5 AUTHOR DETAILS
The research study for the federated learning market involved the use of extensive secondary sources, directories, and several journals, including Elsevier B.V., IEEE Xplore, and Journal of Medical Internet Research (JMIR), and blogs, such as Google AI, OpenMined, NVIDIA, and IBM, to identify and collect information useful for this comprehensive market research study. Primary sources were industry experts from the core and related industries, preferred federated learning providers, third-party service providers, consulting service providers, end users, and other commercial enterprises. In-depth interviews were conducted with various primary respondents, including key industry participants and subject matter experts, to obtain and verify critical qualitative and quantitative information, and assess the market’s prospects.
Secondary Research
In the secondary research process, various sources included annual reports, press releases, and investor presentations of companies; white papers, journals, and certified publications; and articles from recognized authors, directories, and databases. The data was also collected from other secondary sources, such as Elsevier B.V., IEEE Xplore, and Journal of Medical Internet Research (JMIR), and blogs, such as Google AI, OpenMined, NVIDIA, and IBM, magazines such as Analytics India Magazine, HealthTech magazine, and other magazines.
Primary Research
In the primary research process, various primary sources from both supply and demand sides were interviewed to obtain qualitative and quantitative information on the market. The primary sources from the supply side included various industry experts, including Chief Experience Officers (CXOs); Vice Presidents (VPs); directors from business development, marketing, and product development/innovation teams; related key executives from federated learning solution vendors, SIs, professional service providers, and industry associations; and key opinion leaders. Primary interviews were conducted to gather insights, such as market statistics, revenue data collected from solutions and services, market breakups, market size estimations, market forecasts, and data triangulation.
The following is the breakup of primary profiles:
To know about the assumptions considered for the study, download the pdf brochure
Market Size Estimation
The federated learning market is in an initial stage, with a very limited number of available deployments, and a limited number of vendors. Available secondary data as well as primary information was analyzed to identify use cases, research projects, initiatives, and consortiums specific to the market. An exhaustive list of all vendors offering solutions or having initiatives/research projects in the market was prepared. All players do not have solution offerings, whereas some key players such as Cloudera, IBM, and Google are working on research projects to further explore the potential of the federated learning market. The revenue contribution of the market vendors who have direct offerings was estimated through annual reports, press releases, funding, investor presentations, paid databases, and primary interviews. Each vendor's offerings were evaluated on the basis of breadth of applications and verticals. On the other hand, the vendors working on research projects were studied in detail to identify their progress and understand the future scope of federated learning solutions. The markets were triangulated through both primary and secondary research. The primary procedure included extensive interviews for key insights from industry leaders, such as CIOs, CEOs, VPs, directors, and marketing executives. The market numbers were further triangulated with the existing MarketsandMarkets’ repository for validation. The list of vendors considered for estimating the market size is not limited to the vendors profiled in the report.
The pricing trend is assumed to vary over time.
- All the forecasts are made with the standard assumption that the accepted currency is USD.
- For the conversion of various currencies to USD, average historical exchange rates are used according to the year specified. For all the historical and current exchange rates required for calculations and currency conversions, the US Internal Revenue Service's website is used.
- All the forecasts are made under the standard assumption that the globally accepted currency USD remains constant during the next five years.
Data Triangulation
After arriving at the overall market size using the market size estimation processes as explained above, . The market numbers were further triangulated with the existing MarketsandMarkets’ repository for validation. The list of vendors considered for estimating the market size is not limited to the vendors profiled in the report. However, MarketsandMarkets prepared a laundry list of vendors offering edge AI software and ML solutions, and mapped their products related to the federated learning market to identify major vendors operating in the market. The likelihood of these vendors venturing into market is high as they already have ML and edge AI software-specific offerings and federated learning solutions can enable further efficiencies.
Report Objectives
- To define, describe, and predict the federated learning market by region
- To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth
- To analyze opportunities in the market and provide details related to the different vendors operating and working on research projects in the federated learning market
- To forecast the market size of segments with respect to five main regions: North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America
- To profile key players and comprehensively analyze their core competencies
- To analyze competitive developments, such as partnerships, new product launches, and mergers and acquisitions, in the federated learning market
- To analyze different applications of federated learning across verticals
Available Customizations
With the given market data, MarketsandMarkets offers customizations as per the company’s specific needs. The following customization options are available for the report:
Product Analysis
- Product matrix provides a detailed comparison of the product portfolio of each company
Company Information
- Detailed analysis and profiling of additional market players up to 5
- Triangulate with your Own Data
- Get Data as per your Format and Definition
- Gain a Deeper Dive on a Specific Application, Geography, Customer or Competitor
- Any level of Personalization
- What are the Known and Unknown Adjacencies Impacting the Federated Learning Market
- What will your New Revenue Sources be?
- Who will be your Top Customer; what will make them switch?
- Defend your Market Share or Win Competitors
- Get a Scorecard for Target Partners
Growth opportunities and latent adjacency in Federated Learning Market