Federated Learning Solutions Market by Application (Drug Discovery, Industrial IoT), Vertical (Healthcare and Life Sciences, BFSI, Manufacturing, Retail and eCommerce, Energy and Utilities), and Region - Global Forecast to 2028
[145 Pages Report] As per AS-IS scenario, the global federated learning solutions market size to grow from USD 117 million in 2023 to USD 201 million by 2028, at a Compound Annual Growth Rate (CAGR) of 11.4% during the forecast period. Various factors such as the potential to enable companies to leverage a shared ML model collaboratively by keeping data on devices and the capability to enable predictive features on smart devices without impacting user experience and leaking private information are expected to offer growth opportunities for federated learning solutions during the forecast period.
Market Dynamics
Driver: Growing need to increase learning between devices and organization
Federated learning is revolutionizing how ML models are trained. Companies are focusing on spending time on deep research on federated learning. 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. Through federated learning, companies might improve their models and enhance their AI applications. In the healthcare field, federated learning could help healthcare employees to better treat patients and faster drug discovery. Federated learning inside a company or an organization is usually motivated by regulations, such as federated learning across departments of a company. However, in various cases, parties cannot be enforced to provide their data by regulations. Federated learning solutions enable hospitals to reduce strain on the network and enable private learning between various devices or organizations.
Restraint: Lack of skilled technical expertise
The major issue confronting most organizations while incorporating ML in their business processes is the lack of skilled employees, including IT experts. Since federated learning is a new concept, it becomes difficult for employees to understand and implement federated learning models for training data. This is due to the lack of training provided to employees for implementing federated learning models. Recruiting and retaining technical resources have become a significant focus for several enterprises due to the lack of skilled people to develop and execute federated learning projects that involve complex techniques, such as ML. To develop more specialized skill sets and job descriptions as an industry. For example, organizations need engineers who can handle and understand the new federated learning architecture involved with deploying and maintaining ML models.
Opportunity: Potential to enable companies to leverage a shared ML model collaboratively by keeping data on devices
Federated learning is a paradigm in which 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 models are trained on-device. The trained models are transferred to a central server and combined. The standard ML models require centralizing the training data on one machine or in a datacenter. The approach of federated learning distributes the ML process. It enables companies to collaboratively learn a shared model using the training data on the device and keeping the data on the device. It decouples the need for the ML algorithm from the need to store the data in the cloud.
Challenge: Issue of high latency and communication inefficiency
In the era of advanced technologies, mobile devices are equipped with computing and sensing capabilities that gather excessive amounts of data. These amounts of data are suitable for training different learning models. Learning models empower numerous useful applications; for instance, image processing, speech recognition, and vehicular network. Traditionally, ML approaches require data to be centralized in cloud-based datacenters. However, this data is often large in quantity and privacy-sensitive, which prevents logging into these data centers for training learning models. This, in turn, results in critical issues of high latency and communication inefficiency. In federated learning, mobile users are empowered to learn a global model by aggregating their local models without sharing the privacy-sensitive data. Usually, mobile users have slow network connections to the data center where the global model is maintained. In a complex and large-scale network, heterogeneous devices that have various communication constraints are involved. Communication also increases the bandwidth, particularly on consumer mobile connections. This raises the challenge of communication costs when implementing federated learning on a large scale.
As per AS-IS scenario, among verticals, the manufacturing 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, and other verticals (telecommunications and IT, media and entertainment, and government). As per AS-IS scenario, the healthcare and life sciences vertical is expected to account for the largest market size during the forecast period. Moreover, the manufacturing vertical is expected to grow at the highest CAGR during the forecast period. With the increasing focus on Industrial Internet of Things (IIoT) and the rise in competition, manufacturing companies are prioritizing the analysis of data collected from numerous sources, including web, mobile, stores, and social media.
Europe to hold the largest market size during the forecast period
As per AS-IS scenario, Europe, followed by North America, is estimated to account for the largest market size in the federated learning solutions market during the forecast period respectively. Stringent data regulations and high focus on data privacy, focus on innovation through research, and rapid technology infrastructure advancements across verticals are the factors expected to drive the growth. These regions are early adopters of technologies and home to most of the existing federated learning solutions providers. The federated learning solutions market in APAC is projected to grow at the highest CAGR from 2023 to 2028. The increase in the adoption of emerging technologies, such as big data analytics, AI, and IoT, and ongoing developments to introduce data regulations, as well as focus on hyper-personalization and contextual recommendation in support of budding eCommerce markets in key countries such as China, India, and Japan are expected to drive the growth of federated learning solutions in the region.
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), Owkin (US), Intellegens (UK), DataFleets (US), Edge Delta (US), Enveil (US), Lifebit (UK), Secure AI Labs (US), Sherpa.ai (Spain), Decentralized Machine Learning (Singapore), and Consilient (US).
Scope of the Report
|
Report Metric |
Details |
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Market size available for years |
2023–2028 |
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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, and RoW |
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Companies covered |
NVIDIA (US), Cloudera (US), IBM (US), Microsoft (US), Google (US), Owkin (US), Intellegens (UK), DataFleets (US), Edge Delta (US), Enveil (US), Lifebit (UK), Secure AI Labs (US), Sherpa.ai (Spain), Decentralized Machine Learning (Singapore), and Consilient (US) |
This research report categorizes the federated learning solutions market based on application, vertical, and region.
By application:
- Drug Discovery
- Data privacy and Security Management
- Risk Management
- Shopping Experience Personalization
- Industrial Internet of Things (IIoT)
- Online Visual Object Detection
- Other Applications (video analytics, corporate IT, genomics, and anomaly detection)
By vertical:
- BFSI
- Healthcare and Life Sciences
- Retail and eCommerce
- Manufacturing
- Energy and Utilities
- Other Verticals (Telecommunications and IT, Media and Entertainment, and Government)
By region:
- North America
- Europe
- APAC
- RoW
Recent Developments:
- In March 2021, NVIDIA launched the NVIDIA AI Enterprise, a comprehensive software suite of enterprise-grade AI tools and frameworks optimized, certified, and supported by NVIDIA that run on VMware vSphere. NVIDIA AI Enterprise enables customers to reduce AI model development time from 80 weeks to just eight weeks and allows them to deploy and manage advanced AI applications on VMware vSphere.
- 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.
- In November 2020, NVIDIA Clara Train 3.1 introduces a flexible authorization framework that enhances security to ensure sensitive data is protected. It also includes a new administration tool that enables a 10x increase in algorithm experimentation to boost researcher productivity. Clara Train 3.1 new features help healthcare developers scale federated learning securely and boost research productivity.
- In October 2020, LiveRamp acquired DataFleets. The acquisition of DataFleets will enable LiveRamp customers to gain access to a powerful set of privacy-preserving technologies that can be configured based on business needs. With the acquisition, LiveRamp’s products and platforms will invest in exploring data collaboration solutions to keep consumer privacy at the forefront, accelerate planned innovations to the customer experience, and stay ahead of the competition.
- In May 2020, Owkin launched the COVID-19 Open AI Consortium (COAI). The consortium will enable advanced collaborative research and accelerate the clinical development of effective treatments for patients who are infected with COVID-19. In this project, Owkin used federated learning, aiming to help healthcare companies understand why drug efficacy varies from patient-to-patient, enhance the drug development process, and identify the best drug for the right patient at the right time, to improve treatment outcomes.
- In May 2020, IBM collaborated with NVIDIA to accelerate edge analytics and deploy applications at the edge. It will enable clients to unleash the power of accelerated AI computing at the edge with NVIDIA’s easy-to-deploy cloud-native software stack.
- In February 2020, A new consortium of the EU Innovative Medicines Initiative (IMI) was introduced to accelerate the development of AI in medicine. The project called BIGPICTURE will go on for a period of six years. Project participants will be leading European research centers, hospitals as well as major pharmaceutical industries. Owkin participated in the BIGPICTURE consortium through developing AI models to unveil signatures from Whole-Slide Images (WSIs). This will include model training toward predicting outcomes, such as genetic mutations, treatment response, recurrence, survival, and more. Owkin federated learning technologies will enable the collaborative development of AI models.
- In February 2020, Edge Delta partnered with Snowflake. The partnership introduced a new approach where Edge Delta's federated learning is connected with the infinite scalability of Snowflake’s cloud data platform. With this partnership, customers will no longer have to pick and choose subsets of data to enable real-time monitoring and security alerting. Instead, they can analyze that data 100X faster and improve privacy and security posture.
- In December 2019, NVIDIA partnered with Owkin and King’s College London. The aim of the partnership is to protect patient’s data through federated learning in the healthcare and life sciences sector. This partnership brings together the best players in life science and healthcare, ML, and data center infrastructure..
- In January 2019, Cloudera merged with Hortonworks. With this merger, Cloudera will deliver the first enterprise data cloud - unlocking the power of any data, running in any cloud from the Edge to AI, on a 100% open-source data platform. The merger will enable the Cloudera team to provide customers with a comprehensive solution-set to bring the right data analytics to data anywhere the enterprise needs to work, from the Edge to AI, Enterprise Data Cloud.
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.
According to NEC, federated learning is a new ML technique that can train an AI model across edge clients holding local data samples without exchanging them. It is expected to solve the data security/privacy issue in training AI model.
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, 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, 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. .
To speak to our analyst for a discussion on the above findings, click Speak to Analyst
TABLE OF CONTENTS
1 INTRODUCTION (Page No. - 19)
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, 2018–2020
1.5 STAKEHOLDERS
2 RESEARCH METHODOLOGY (Page No. - 23)
2.1 RESEARCH DATA
FIGURE 1 FEDERATED LEARNING SOLUTIONS MARKET: RESEARCH DESIGN
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
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 FEDERATED LEARNING SOLUTIONS MARKET: MARKET ESTIMATION APPROACH
2.4 MARKET FORECAST
TABLE 2 CRITICAL FACTORS IMPACTING THE MARKET GROWTH
2.5 ASSUMPTIONS FOR THE STUDY
2.6 LIMITATIONS OF THE STUDY
3 EXECUTIVE SUMMARY (Page No. - 31)
3.1 FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 4 GLOBAL FEDERATED LEARNING SOLUTIONS MARKET, 2023–2028 (USD THOUSANDS)
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 IN 2023
3.2 SUMMARY OF KEY FINDINGS
4 MARKET OVERVIEW AND INDUSTRY TRENDS (Page No. - 36)
4.1 INTRODUCTION
4.2 FEDERATED LEARNING: TYPES
FIGURE 7 TYPES OF FEDERATED LEARNING
4.3 FEDERATED LEARNING: EVOLUTION
FIGURE 8 EVOLUTION OF FEDERATED LEARNING SOLUTIONS MARKET
4.4 FEDERATED LEARNING: ARCHITECTURE
FIGURE 9 ARCHITECTURE OF FEDERATED LEARNING
4.5 ARTIFICIAL INTELLIGENCE: ECOSYSTEM
FIGURE 10 ARTIFICIAL INTELLIGENCE ECOSYSTEM
4.6 RESEARCH PROJECTS: FEDERATED LEARNING
4.6.1 MACHINE LEARNING LEDGER ORCHESTRATION FOR DRUG DISCOVERY (MELLODDY)
4.6.1.1 Participants
4.6.2 FEDAI
4.6.3 PADDLEPADDLE
4.6.4 FEATURECLOUD
4.6.5 MUSKETEER PROJECT
4.7 MARKET DYNAMICS
FIGURE 11 DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES: FEDERATED LEARNING SOLUTIONS MARKET
4.7.1 DRIVERS
4.7.1.1 Growing need to increase learning between devices and organization
4.7.1.2 Ability to ensure better data privacy and security by training algorithms on decentralized devices
4.7.2 RESTRAINTS
4.7.2.1 Lack of skilled technical expertise
4.7.3 OPPORTUNITIES
4.7.3.1 Potential to enable companies to leverage a shared ML model collaboratively by keeping data on devices
4.7.3.2 Capability to enable predictive features on smart devices without impacting user experience and leaking private information
4.7.4 CHALLENGES
4.7.4.1 Issues of high latency and communication inefficiency
4.7.4.2 System heterogeneity and issue in interoperability
4.7.4.3 Indirect information leakage
4.8 IMPACT OF DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES ON THE FEDERATED LEARNING SOLUTIONS MARKET
4.9 USE CASE ANALYSIS
4.9.1 WEBANK AND A CAR RENTAL SERVICE PROVIDER ENABLE INSURANCE INDUSTRY TO REDUCE DATA TRAFFIC VIOLATIONS THROUGH FEDERATED LEARNING
4.9.2 FEDERATED LEARNING ENABLE HEALTHCARE COMPANIES TO ENCRYPT AND PROTECT PATIENT DATA
4.9.3 WEBANK AND EXTREME VISION INTRODUCED ONLINE VISUAL OBJECT DETECTION PLATFORM POWERED BY FEDERATED LEARNING TO STORE DATA IN CLOUD
4.9.4 WEBANK INTRODUCED FEDERATED LEARNING MODEL FOR ANTI-MONEY LAUNDERING
4.9.5 INTELLEGENS SOLUTION ADOPTION MAY HELP CLINICALS ANALYZE HEART RATE DATA
4.1 PATENT ANALYSIS
4.10.1 METHODOLOGY
4.10.2 DOCUMENT TYPE
TABLE 3 PATENTS FILED
4.10.3 INNOVATION AND PATENT APPLICATIONS
FIGURE 12 TOTAL NUMBER OF PATENTS GRANTED IN A YEAR, 2015–2021
4.10.3.1 Top applicants
FIGURE 13 TOP 10 COMPANIES WITH THE HIGHEST NUMBER OF PATENT APPLICATIONS, 2015–2021
TABLE 4 TOP EIGHT PATENT OWNERS (US) IN THE FEDERATED LEARNING SOLUTIONS MARKET, 2015–2021
4.11 SUPPLY CHAIN ANALYSIS
FIGURE 14 SUPPLY CHAIN ANALYSIS
4.12 TECHNOLOGY ANALYSIS
4.12.1 FEDERATED LEARNING VS DISTRIBUTED MACHINE LEARNING
4.12.2 FEDERATED LEARNING VS EDGE COMPUTING
4.12.3 FEDERATED LEARNING VS FEDERATED DATABASE SYSTEMS
4.12.4 FEDERATED LEARNING VS SWARM LEARNING
5 FEDERATED LEARNING SOLUTIONS MARKET, BY APPLICATION (Page No. - 56)
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
5.3 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
5.4 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
5.5 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
5.6 INDUSTRIAL INTERNET OF THINGS
5.6.1 FEDERATED LEARNING SOLUTIONS ENABLE PREDICTIVE MAINTENANCE ON EDGE DEVICES WITHOUT CENTRALIZING DATA AND INCREASE OPERATIONAL EFFICIENCY
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
5.8 OTHER APPLICATIONS
6 FEDERATED LEARNING SOLUTIONS MARKET, BY VERTICAL (Page No. - 62)
6.1 INTRODUCTION
TABLE 5 PESSIMISTIC SCENARIO: MARKET SIZE, BY VERTICAL, 2023–2028 (USD THOUSANDS)
TABLE 6 AS-IS SCENARIO: MARKET SIZE, BY VERTICAL, 2023–2028 (USD THOUSANDS)
TABLE 7 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 15 BANKING, FINANCIAL SERVICES, AND INSURANCE: THE FEDERATED LEARNING SOLUTIONS 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 16 HEALTHCARE AND LIFE SCIENCES: THE 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 17 RETAIL AND ECOMMERCE: THE FEDERATED LEARNING SOLUTIONS 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 18 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 19 ENERGY AND UTILITIES: THE MARKET, 2023–2028 (USD THOUSANDS)
6.7 OTHER VERTICALS
7 FEDERATED LEARNING SOLUTIONS MARKET, BY REGION (Page No. - 73)
7.1 INTRODUCTION
TABLE 8 PESSIMISTIC SCENARIO: MARKET SIZE, BY REGION, 2023–2028 (USD THOUSANDS)
TABLE 9 AS-IS SCENARIO: MARKET SIZE, BY REGION, 2023–2028 (USD THOUSANDS)
TABLE 10 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: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 20 NORTH AMERICA: THE FEDERATED LEARNING SOLUTIONS MARKET, 2023–2028 (USD THOUSANDS)
7.2.3 NORTH AMERICA: REGULATIONS
7.2.3.1 Health Insurance Portability and Accountability Act of 1996
7.2.3.2 California Consumer Privacy Act
7.2.3.3 Gramm–Leach–Bliley Act
7.2.3.4 Health Information Technology for Economic and Clinical Health Act
7.2.3.5 Federal Information Security Management Act
7.2.3.6 Payment Card Industry Data Security Standard
7.2.3.7 Federal Information Processing Standards
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: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 21 EUROPE: THE FEDERATED LEARNING SOLUTIONS MARKET, 2023–2028 (USD THOUSANDS)
7.3.3 EUROPE: REGULATIONS
7.3.3.1 General Data Protection Regulation
7.3.3.2 European Committee for Standardization
7.3.3.3 European Technical Standards Institute
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: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 22 ASIA PACIFIC: THE FEDERATED LEARNING SOLUTIONS MARKET, 2023–2028 (USD THOUSANDS)
7.4.3 ASIA PACIFIC: REGULATIONS
7.4.3.1 Privacy Commissioner for Personal Data
7.4.3.2 Act on the Protection of Personal Information
7.4.3.3 Critical Information Infrastructure
7.4.3.4 International Organization for Standardization 27001
7.4.3.5 Personal Data Protection Act
7.5 REST OF WORLD
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 REST OF WORLD: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 23 REST OF WORLD: THE FEDERATED LEARNING SOLUTIONS MARKET, 2023–2028 (USD THOUSANDS)
7.5.3 MIDDLE EAST AND AFRICA: REGULATIONS
7.5.3.1 Israeli Privacy Protection Regulations (Data Security), 5777-2017
7.5.3.2 Cloud Computing Framework
7.5.3.3 GDPR Applicability in the Kingdom of Saudi Arabia (KSA)
7.5.3.4 Protection of Personal Information Act
7.5.4 LATIN AMERICA: REGULATIONS
7.5.4.1 Brazil Data Protection Law
7.5.4.2 Argentina Personal Data Protection Law No. 25.326
8 COMPANY PROFILES (Page No. - 88)
8.1 INTRODUCTION
(Business Overview, Solutions, Key Insights, Recent Developments, MnM View)*
8.2 NVIDIA
TABLE 11 NVIDIA: BUSINESS OVERVIEW
FIGURE 24 NVIDIA: COMPANY SNAPSHOT
TABLE 12 NVIDIA: FEDERATED LEARNING SOLUTIONS MARKET: SOLUTION LAUNCHES AND ENHANCEMENTS
TABLE 13 NVIDIA: MARKET: DEALS
FIGURE 25 BUSINESS MODEL CANVAS: NVIDIA
8.3 CLOUDERA
TABLE 14 CLOUDERA: BUSINESS OVERVIEW
FIGURE 26 CLOUDERA: COMPANY SNAPSHOT
TABLE 15 CLOUDERA: MARKET: SOLUTION LAUNCHES AND ENHANCEMENTS
TABLE 16 CLOUDERA: MARKET: DEALS
FIGURE 27 BUSINESS MODEL CANVAS: CLOUDERA
8.4 IBM
TABLE 17 IBM: BUSINESS OVERVIEW
FIGURE 28 IBM: COMPANY SNAPSHOT
TABLE 18 IBM: FEDERATED LEARNING SOLUTIONS MARKET: RESEARCH PROJECT
TABLE 19 IBM: MARKET: DEALS
FIGURE 29 BUSINESS MODEL CANVAS: IBM
8.5 MICROSOFT
TABLE 20 MICROSOFT: BUSINESS OVERVIEW
FIGURE 30 MICROSOFT: COMPANY SNAPSHOT
TABLE 21 MICROSOFT: MARKET: RESEARCH PROJECT
TABLE 22 MICROSOFT: MARKET: SOLUTION LAUNCHES AND ENHANCEMENTS
TABLE 23 MICROSOFT: MARKET: DEALS
FIGURE 31 BUSINESS MODEL CANVAS: MICROSOFT
8.6 GOOGLE
TABLE 24 GOOGLE: BUSINESS OVERVIEW
FIGURE 32 GOOGLE: COMPANY SNAPSHOT
TABLE 25 GOOGLE: FEDERATED LEARNING SOLUTIONS MARKET: RESEARCH PROJECT
TABLE 26 GOOGLE: MARKET: SOLUTION LAUNCHES AND ENHANCEMENTS
FIGURE 33 BUSINESS MODEL CANVAS: GOOGLE
8.7 OWKIN
TABLE 27 OWKIN: MARKET: RESEARCH PROJECT AND FUNDING
TABLE 28 OWKIN: MARKET: DEALS
8.8 INTELLEGENS
TABLE 29 INTELLEGENS: MARKET: RESEARCH PROJECT AND FUNDING
8.9 DATAFLEETS
TABLE 30 DATAFLEETS: MARKET: RESEARCH PROJECT AND FUNDING
TABLE 31 DATAFLEETS: MARKET: DEALS
8.10 EDGE DELTA
TABLE 32 EDGE DELTA: MARKET: RESEARCH PROJECT AND FUNDING
TABLE 33 EDGE DELTA: MARKET: DEALS
8.11 ENVEIL
TABLE 34 ENVEIL: FEDERATED LEARNING SOLUTIONS MARKET: RESEARCH PROJECT AND FUNDING
TABLE 35 ENVEIL: MARKET: SOLUTION LAUNCHES AND ENHANCEMENTS
8.12 LIFEBIT
TABLE 36 LIFEBIT: MARKET: RESEARCH PROJECT AND FUNDING
TABLE 37 LIFEBIT: MARKET: SOLUTION LAUNCHES AND ENHANCEMENTS
8.13 SECURE AI LABS
8.14 SHERPA.AI
8.15 DECENTRALIZED MACHINE LEARNING
8.16 CONSILIENT
*Details on Business Overview, Solutions, Key Insights, Recent Developments, MnM View might not be captured in case of unlisted companies.
8.17 COMPETITIVE BENCHMARKING
TABLE 38 COMPETITIVE BENCHMARKING: OFFERINGS AND REGIONAL PRESENCE
TABLE 39 COMPETITIVE BENCHMARKING: TARGET VERTICALS
9 ADJACENT AND RELATED MARKETS (Page No. - 127)
9.1 INTRODUCTION
9.2 MACHINE LEARNING MARKET - GLOBAL FORECAST TO 2022
9.2.1 MARKET DEFINITION
9.2.2 MARKET OVERVIEW
TABLE 40 GLOBAL MACHINE LEARNING MARKET SIZE AND GROWTH RATE, 2015–2022 (USD MILLION, Y-O-Y %)
9.2.2.1 Machine learning market, by vertical
TABLE 41 MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
9.2.2.2 Machine learning market, by deployment mode
TABLE 42 MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
9.2.2.3 Machine learning market, by organization size
TABLE 43 MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
9.2.2.4 Machine learning market, by service
TABLE 44 MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
9.2.2.5 Machine learning market, by region
TABLE 45 MACHINE LEARNING MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
9.3 EDGE AI SOFTWARE MARKET - GLOBAL FORECAST TO 2026
9.3.1 MARKET DEFINITION
9.3.2 MARKET OVERVIEW
TABLE 46 GLOBAL EDGE AI SOFTWARE MARKET SIZE AND GROWTH RATE, 2014–2019 (USD MILLION, Y-O-Y%)
TABLE 47 GLOBAL EDGE AI SOFTWARE MARKET SIZE AND GROWTH RATE, 2019–2026 (USD MILLION, Y-O-Y%)
9.3.2.1 Edge AI software market, by component
TABLE 48 EDGE AI SOFTWARE MARKET SIZE, BY COMPONENT, 2014–2019 (USD MILLION)
TABLE 49 EDGE AI SOFTWARE MARKET SIZE, BY COMPONENT, 2019–2026 (USD MILLION)
9.3.2.2 Edge AI software market, by data source
TABLE 50 EDGE AI SOFTWARE MARKET SIZE, BY DATA SOURCE, 2014–2019 (USD MILLION)
TABLE 51 EDGE AI SOFTWARE MARKET SIZE, BY DATA SOURCE, 2019–2026 (USD MILLION)
9.3.2.3 Edge AI software market, by application
TABLE 52 EDGE AI SOFTWARE MARKET SIZE, BY APPLICATION, 2014–2019 (USD MILLION)
TABLE 53 EDGE AI SOFTWARE MARKET SIZE, BY APPLICATION, 2019–2026 (USD MILLION)
9.3.2.4 Edge AI software market, by vertical
TABLE 54 EDGE AI SOFTWARE MARKET SIZE, BY VERTICAL, 2014–2019 (USD MILLION)
TABLE 55 EDGE AI SOFTWARE MARKET SIZE, BY VERTICAL, 2019–2026 (USD MILLION)
9.3.2.5 Edge AI software market, by region
TABLE 56 EDGE AI SOFTWARE MARKET SIZE, BY REGION, 2014–2019 (USD MILLION)
TABLE 57 EDGE AI SOFTWARE MARKET SIZE, BY REGION, 2019–2026 (USD MILLION)
10 APPENDIX (Page No. - 137)
10.1 INDUSTRY EXPERTS
10.2 DISCUSSION GUIDE
10.3 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
10.4 AVAILABLE CUSTOMIZATIONS
10.5 RELATED REPORTS
10.6 AUTHOR DETAILS
The study involved four major activities in estimating the current market size of federated learning solutions market. Extensive secondary research was done to collect information on the market, peer market, and parent market. The next step was to validate these findings, assumptions, and sizing with industry experts across the value chain through primary research. The market breakup and data triangulation procedures were used to estimate the market size of the segments and subsegments of the federated learning solutions market. A conservative approach has been followed and the market size has been factored on the basis of available solutions, research initiatives, and associated markets
Secondary Research
In the secondary research process, various 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 have been referred to for identifying and collecting information for this study. Secondary sources included annual reports; press releases & investor presentations of companies; whitepapers, certified publications, and articles by recognized authors; gold standard and silver standard websites; Research and Development (R&D) organizations; regulatory bodies; and databases.
Primary Research
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 X Officers (CXOs); Vice Presidents (VPs); directors from business development, marketing, and product development/innovation teams; related key executives from federated learning solution vendors, system integrators, professional service providers, industry associations, and consultants; and key opinion leaders. All possible parameters that affect the market covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.
The following is the breakup of primary profiles:
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Market Size Estimation
The federated learning solutions market is in an initial stage, with a limited number of available deployments and vendors. The available secondary data, as well as primary information, was analyzed to identify use cases, research projects, initiatives, and consortiums specific to the federated learning solutions market. The research methodology used to estimate the market size includes the following:
- The key players in the market were identified through extensive secondary research.
- The market size, in terms of value, was determined through primary and secondary research processes.
- All percentages, shares, and breakups were determined using secondary sources and verified through primary sources.
Data Triangulation
After arriving at the overall market size using the market size estimation processes as explained above, the market was split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation, and market breakup procedures were employed, wherever applicable. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segmentation.
Report Objectives
- To define, describe, and predict the federated learning solutions 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 of the federated learning solutions market
- To forecast the market size of segments with respect to four main regions: North America, Europe, Asia Pacific (APAC), Rest of World (RoW).
- 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 solutions 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
Geographic analysis
- Further breakup of the North American federated learning solutions market
- Further breakup of the European market
- Further breakup of the APAC market
- Further breakup of the RoW market
Company information
- Detailed analysis and profiling of additional market players up to 5
Get in-depth analysis of the COVID-19 impact on the Federated Learning Solutions Market
Benchmarking the rapid strategy shifts of the Top 100 companies in the Federated Learning Solutions Market
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Growth opportunities and latent adjacency in Federated Learning Solutions Market