Federated Learning Solutions Market

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

Report Code: TC 7866 Apr, 2021, by marketsandmarkets.com

[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 Machine Learning (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.

Federated Learning Solutions Market

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Market Dynamics

Driver: Ability to ensure better data privacy and security by training algorithms on decentralized devices

Federated learning is being researched by major companies and plays a critical role in supporting privacy-sensitive applications where the training data are distributed at the edge. Federated learning takes a step toward protecting users’ data by sharing model updates. Companies can no longer ignore the growing importance of data privacy and data security. The approach of federated learning has provided a new paradigm for applications leveraging data. Currently, data silos and the focus on data privacy are important challenges for AI, but federated learning could be a solution. It could establish a united model for multiple organizations while the local and sensitive data is protected so that they could benefit together without having to worry about data privacy. Federated learning has received a lot of attention in the way the technology tackles the challenge of protecting users’ privacy by decoupling of data provisioned at end-user equipment and Machine Learning (ML) model aggregation, such as network parameters of deep learning at a centralized server. With federated learning, privacy can be classified in two ways: global privacy and local privacy. Global privacy necessitates that the model updates generated at each round are private to all untrusted third parties other than the central server. At the same time, local privacy further requires that the updates are also private to the server.

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: Capability to enable predictive features on smart devices without impacting user experience and leaking private information

Mobile phones, wearable devices, and autonomous vehicles are just a few of the modern distributed networks generating a wealth of data each day. Owing to the growing computational power of these devices—coupled with concerns related to transmitting private information—it is increasingly attractive to store data locally and push network computation to the edge devices. Federated learning is an emerging approach that helps companies easily collect and store data. Federated learning has the potential to enable predictive features on smartphones without diminishing the user experience or leaking private information. Edge devices, such as smartphones and IoT devices, can benefit from the on-device data without the data ever leaving the device, especially for computationally constrained devices where communication is a bottleneck with smaller devices. Today, industries, such as BFSI, healthcare and life sciences, and retail and eCommerce, collect gigantic amounts of data generated by consumer devices, including mobile phones, tablets, and personal laptops, on a daily basis. The federated learning approach provides a unique way to build such personalized models without intruding users’ privacy.

Challenge: Indirect information leakage

Privacy concerns serve to motivate the desire to keep raw data on each local device in a distributed Machine Learning (ML) setting. However, sharing other information such as model updates as part of the training process brings up another concern—the potential to leak sensitive user information. For instance, it is possible to extract sensitive text patterns, such as a credit card number, from a Recurrent Neural Network (RNN) trained on the user data. Unlike differential privacy protection, the data and the model itself are not transmitted, nor can they be guessed by the other party’s data. Hence, there is a little possibility of leakage at the raw data level. Federated learning exposes intermediate results, such as parameter updates from an optimization algorithm, such as Stochastic Gradient Descent (SGD). However, no security guarantee is provided, and the leakage of these gradients may actually reveal important information when exposed together with data structure, such as in the case of image pixels.

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.

Federated Learning Solutions Market by 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

Market size available for years

2023–2028

Forecast period

2023–2028

Forecast units

 USD Thousands

Segments covered

Application, vertical, and region

Geographies covered

North America, Europe, APAC, and RoW

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.

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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:

Federated Learning Solutions Market Size, and Share

To know about the assumptions considered for the study, download the pdf brochure

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 federated learning solutions market
  • Further breakup of the APAC federated learning solutions market
  • Further breakup of the RoW federated learning solutions market

Company information

  • Detailed analysis and profiling of additional market players up to 5
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Report Code
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Published ON
Apr, 2021
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