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

世界の連合学習ソリューション市場予測:用途別、産業別、地域別

◆タイトル: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
◆商品コード:TC7866
◆調査・発行会社:MarketsandMarkets
◆発行日:2029年4月15日
◆ページ数:143
◆レポート形式:PDF / 英語
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【レポートの概要】

マーケッツアンドマーケッツ社は、世界の連合学習ソリューション市場規模が2023年117百万ドルから2028年201百万ドルまで、年平均11.4%成長すると予測しています。本調査レポートでは、連合学習ソリューションの世界市場について調査・分析し、イントロダクション、調査手法、エグゼクティブサマリー、プレミアムインサイト、市場概要・産業動向、用途別(買い物体験パーソナライゼーション、データプライバシー&セキュリティ管理、リスク管理、産業用IoT、オンライン視覚的対象物検出)分析、産業別(金融、医療&ライフサイエンス、小売&Eコマース、製造、エネルギー&ユーティリティ)分析、地域別分析、企業情報、隣接・関連市場などの項目を掲載しています。
・イントロダクション
・調査手法
・エグゼクティブサマリー
・プレミアムインサイト
・市場概要・産業動向
・世界の連合学習ソリューション市場規模:用途別(買い物体験パーソナライゼーション、データプライバシー&セキュリティ管理、リスク管理、産業用IoT、オンライン視覚的対象物検出)
・世界の連合学習ソリューション市場規模:産業別(金融、医療&ライフサイエンス、小売&Eコマース、製造、エネルギー&ユーティリティ)
・世界の連合学習ソリューション市場規模:地域別
・企業情報
・隣接・関連市場

“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.

“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.

“As per AS-IS scenario, among regions, Asia Pacific (APAC) to grow at the highest CAGR during the forecast period”
As per AS-IS scenario, 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.

Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the federated learning solutions market.
 By Company: Tier I: 34%, Tier II: 43%, and Tier III: 23%
 By Designation: C-Level Executives: 50%, Directors: 30%, and Others: 20%
 By Region: North America: 25%, APAC: 30%, Europe: 30%, MEA: 10%, and Latin America: 5%

The report includes the study of key players offering federated learning solutions and research projects. It profiles major vendors in the global federated learning solutions market. The major vendors in the global federated learning solutions market are 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).

Research Coverage
The market study covers the federated learning solutions market across segments. It aims at estimating the market size and the growth potential of this market across different segments, such as verticals and regions. It includes an in-depth competitive analysis of the key players in the market, along with their company profiles, key observations related to product and business offerings, key initiatives, recent developments, and key market strategies.

Key Benefits of Buying the Report
The report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall federated learning solutions market and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights better to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.

【レポートの目次】

1 INTRODUCTION 19
1.1 OBJECTIVES OF THE STUDY 19
1.2 MARKET DEFINITION 19
1.2.1 INCLUSIONS AND EXCLUSIONS 20
1.3 MARKET SCOPE 20
1.3.1 MARKET SEGMENTATION 21
1.3.2 YEARS CONSIDERED FOR THE STUDY 21
1.4 CURRENCY CONSIDERED 21
TABLE 1 UNITED STATES DOLLAR EXCHANGE RATE, 2018–2020 21
1.5 STAKEHOLDERS 22
2 RESEARCH METHODOLOGY 23
2.1 RESEARCH DATA 23
FIGURE 1 FEDERATED LEARNING SOLUTIONS MARKET: RESEARCH DESIGN 23
2.1.1 SECONDARY DATA 23
2.1.2 PRIMARY DATA 24
2.1.2.1 Breakup of primary profiles 24
2.1.2.2 Key industry insights 25
2.2 MARKET BREAKUP AND DATA TRIANGULATION 26
FIGURE 2 DATA TRIANGULATION 26
2.3 MARKET SIZE ESTIMATION 26
FIGURE 3 FEDERATED LEARNING SOLUTIONS MARKET: MARKET ESTIMATION APPROACH 27
2.4 MARKET FORECAST 28
TABLE 2 CRITICAL FACTORS IMPACTING THE MARKET GROWTH 28
2.5 ASSUMPTIONS FOR THE STUDY 29
2.6 LIMITATIONS OF THE STUDY 30
3 EXECUTIVE SUMMARY 31
3.1 FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC) 33
FIGURE 4 GLOBAL FEDERATED LEARNING SOLUTIONS MARKET, 2023–2028
(USD THOUSANDS) 33
FIGURE 5 HEALTHCARE AND LIFE SCIENCES VERTICAL TO HOLD THE LARGEST MARKET SHARE DURING THE FORECAST PERIOD 33
FIGURE 6 EUROPE TO HOLD THE LARGEST MARKET SHARE IN 2023 34
3.2 SUMMARY OF KEY FINDINGS 34

4 MARKET OVERVIEW AND INDUSTRY TRENDS 36
4.1 INTRODUCTION 36
4.2 FEDERATED LEARNING: TYPES 36
FIGURE 7 TYPES OF FEDERATED LEARNING 36
4.3 FEDERATED LEARNING: EVOLUTION 37
FIGURE 8 EVOLUTION OF FEDERATED LEARNING SOLUTIONS MARKET 37
4.4 FEDERATED LEARNING: ARCHITECTURE 38
FIGURE 9 ARCHITECTURE OF FEDERATED LEARNING 38
4.5 ARTIFICIAL INTELLIGENCE: ECOSYSTEM 39
FIGURE 10 ARTIFICIAL INTELLIGENCE ECOSYSTEM 39
4.6 RESEARCH PROJECTS: FEDERATED LEARNING 40
4.6.1 MACHINE LEARNING LEDGER ORCHESTRATION
FOR DRUG DISCOVERY (MELLODDY) 40
4.6.1.1 Participants 40
4.6.2 FEDAI 41
4.6.3 PADDLEPADDLE 42
4.6.4 FEATURECLOUD 42
4.6.5 MUSKETEER PROJECT 42
4.7 MARKET DYNAMICS 43
FIGURE 11 DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES:
FEDERATED LEARNING SOLUTIONS MARKET 43
4.7.1 DRIVERS 43
4.7.1.1 Growing need to increase learning between devices and organization 43
4.7.1.2 Ability to ensure better data privacy and security by training algorithms on decentralized devices 44
4.7.2 RESTRAINTS 45
4.7.2.1 Lack of skilled technical expertise 45
4.7.3 OPPORTUNITIES 45
4.7.3.1 Potential to enable companies to leverage a shared ML model collaboratively by keeping data on devices 45
4.7.3.2 Capability to enable predictive features on smart devices without impacting user experience and leaking private information 46
4.7.4 CHALLENGES 47
4.7.4.1 Issues of high latency and communication inefficiency 47
4.7.4.2 System heterogeneity and issue in interoperability 47
4.7.4.3 Indirect information leakage 48
4.8 IMPACT OF DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES ON THE FEDERATED LEARNING SOLUTIONS MARKET 48
4.9 USE CASE ANALYSIS 49
4.9.1 WEBANK AND A CAR RENTAL SERVICE PROVIDER ENABLE INSURANCE INDUSTRY TO REDUCE DATA TRAFFIC VIOLATIONS THROUGH FEDERATED LEARNING 49
4.9.2 FEDERATED LEARNING ENABLE HEALTHCARE COMPANIES TO ENCRYPT
AND PROTECT PATIENT DATA 49
4.9.3 WEBANK AND EXTREME VISION INTRODUCED ONLINE VISUAL OBJECT DETECTION PLATFORM POWERED BY FEDERATED LEARNING TO STORE DATA IN CLOUD 50
4.9.4 WEBANK INTRODUCED FEDERATED LEARNING MODEL FOR ANTI-MONEY LAUNDERING 50
4.9.5 INTELLEGENS SOLUTION ADOPTION MAY HELP CLINICALS ANALYZE HEART RATE DATA 51
4.10 PATENT ANALYSIS 51
4.10.1 METHODOLOGY 51
4.10.2 DOCUMENT TYPE 51
TABLE 3 PATENTS FILED 51
4.10.3 INNOVATION AND PATENT APPLICATIONS 51
FIGURE 12 TOTAL NUMBER OF PATENTS GRANTED IN A YEAR, 2015–2021 52
4.10.3.1 Top applicants 52
FIGURE 13 TOP 10 COMPANIES WITH THE HIGHEST NUMBER OF PATENT APPLICATIONS, 2015–2021 52
TABLE 4 TOP EIGHT PATENT OWNERS (US) IN THE FEDERATED LEARNING SOLUTIONS MARKET, 2015–2021 53
4.11 SUPPLY CHAIN ANALYSIS 53
FIGURE 14 SUPPLY CHAIN ANALYSIS 53
4.12 TECHNOLOGY ANALYSIS 54
4.12.1 FEDERATED LEARNING VS DISTRIBUTED MACHINE LEARNING 54
4.12.2 FEDERATED LEARNING VS EDGE COMPUTING 54
4.12.3 FEDERATED LEARNING VS FEDERATED DATABASE SYSTEMS 55
4.12.4 FEDERATED LEARNING VS SWARM LEARNING 55
5 FEDERATED LEARNING SOLUTIONS MARKET, BY APPLICATION 56
5.1 INTRODUCTION 57
5.2 DRUG DISCOVERY 57
5.2.1 ABILITY TO ACCELERATE DRUG DISCOVERY BY ENABLING INCREASED COLLABORATIONS FOR FASTER TREATMENT TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS 57
5.3 SHOPPING EXPERIENCE PERSONALIZATION 58
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 58
5.4 DATA PRIVACY AND SECURITY MANAGEMENT 58
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 58
5.5 RISK MANAGEMENT 59
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 59

5.6 INDUSTRIAL INTERNET OF THINGS 60
5.6.1 FEDERATED LEARNING SOLUTIONS ENABLE PREDICTIVE MAINTENANCE ON EDGE DEVICES WITHOUT CENTRALIZING DATA AND INCREASE OPERATIONAL EFFICIENCY 60
5.7 ONLINE VISUAL OBJECT DETECTION 60
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 60
5.8 OTHER APPLICATIONS 61
6 FEDERATED LEARNING SOLUTIONS MARKET, BY VERTICAL 62
6.1 INTRODUCTION 63
TABLE 5 PESSIMISTIC SCENARIO: FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY VERTICAL, 2023–2028 (USD THOUSANDS) 63
TABLE 6 AS-IS SCENARIO: FEDERATED LEARNING SOLUTIONS MARKET SIZE,
BY VERTICAL, 2023–2028 (USD THOUSANDS) 63
TABLE 7 OPTIMISTIC SCENARIO: FEDERATED LEARNING SOLUTIONS MARKET SIZE,
BY VERTICAL, 2023–2028 (USD THOUSANDS) 64
6.2 BANKING, FINANCIAL SERVICES, AND INSURANCE 64
6.2.1 ABILITY TO REDUCE MALICIOUS ACTIVITIES AND PROTECT CUSTOMER DATA TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS IN THE BFSI VERTICAL 64
6.2.2 BANKING, FINANCIAL SERVICES, AND INSURANCE: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC) 65
FIGURE 15 BANKING, FINANCIAL SERVICES, AND INSURANCE: THE FEDERATED LEARNING SOLUTIONS MARKET, 2023–2028 (USD THOUSANDS) 65
6.3 HEALTHCARE AND LIFE SCIENCES 65
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 65
6.3.2 HEALTHCARE AND LIFE SCIENCES: FORECAST 2023–2028
(OPTIMISTIC/AS-IS/PESSIMISTIC) 67
FIGURE 16 HEALTHCARE AND LIFE SCIENCES: THE FEDERATED LEARNING SOLUTIONS MARKET, 2023–2028 (USD THOUSANDS) 67
6.4 RETAIL AND ECOMMERCE 67
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 67
6.4.2 RETAIL AND ECOMMERCE: FORECAST 2023–2028
(OPTIMISTIC/AS-IS/PESSIMISTIC) 68
FIGURE 17 RETAIL AND ECOMMERCE: THE FEDERATED LEARNING SOLUTIONS MARKET, 2023–2028 (USD THOUSANDS) 68
6.5 MANUFACTURING 69
6.5.1 FOCUS ON SMART MANUFACTURING AND NEED FOR ENHANCED OPERATIONAL INTELLIGENCE TO DRIVE THE ADOPTION OF FEDERATED LEARNING ACROSS THE MANUFACTURING VERTICAL 69
6.5.2 MANUFACTURING: FORECAST 2023–2028
(OPTIMISTIC/AS-IS/PESSIMISTIC) 70
FIGURE 18 MANUFACTURING: THE FEDERATED LEARNING SOLUTIONS MARKET,
2023–2028 (USD THOUSANDS) 70
6.6 ENERGY AND UTILITIES 70
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 70
6.6.2 ENERGY AND UTILITIES: FORECAST 2023–2028
(OPTIMISTIC/AS-IS/PESSIMISTIC) 71
FIGURE 19 ENERGY AND UTILITIES: THE FEDERATED LEARNING SOLUTIONS MARKET, 2023–2028 (USD THOUSANDS) 71
6.7 OTHER VERTICALS 71
7 FEDERATED LEARNING SOLUTIONS MARKET, BY REGION 73
7.1 INTRODUCTION 74
TABLE 8 PESSIMISTIC SCENARIO: FEDERATED LEARNING SOLUTIONS MARKET SIZE, BY REGION, 2023–2028 (USD THOUSANDS) 74
TABLE 9 AS-IS SCENARIO: FEDERATED LEARNING SOLUTIONS MARKET SIZE,
BY REGION, 2023–2028 (USD THOUSANDS) 74
TABLE 10 OPTIMISTIC SCENARIO: FEDERATED LEARNING SOLUTIONS MARKET SIZE,
BY REGION, 2023–2028 (USD THOUSANDS) 75
7.2 NORTH AMERICA 75
7.2.1 HIGH FOCUS OF NORTH AMERICAN COMPANIES TOWARD RESEARCH IN FEDERATED LEARNING TO ENABLE FUTURISTIC DATA-TRAINED MODELS 75
7.2.2 NORTH AMERICA: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC) 76
FIGURE 20 NORTH AMERICA: THE FEDERATED LEARNING SOLUTIONS MARKET, 2023–2028 (USD THOUSANDS) 76
7.2.3 NORTH AMERICA: REGULATIONS 76
7.2.3.1 Health Insurance Portability and Accountability Act of 1996 76
7.2.3.2 California Consumer Privacy Act 77
7.2.3.3 Gramm–Leach–Bliley Act 77
7.2.3.4 Health Information Technology for Economic and Clinical Health Act 77
7.2.3.5 Federal Information Security Management Act 77
7.2.3.6 Payment Card Industry Data Security Standard 77
7.2.3.7 Federal Information Processing Standards 78
7.3 EUROPE 79
7.3.1 HIGH FOCUS ON DATA PRIVACY AND COMPLIANCE, AND INCREASED RESEARCH COLLABORATIONS TO DRIVE THE ADOPTION OF FEDERATED LEARNING IN EUROPE 79
7.3.2 EUROPE: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC) 80
FIGURE 21 EUROPE: THE FEDERATED LEARNING SOLUTIONS MARKET,
2023–2028 (USD THOUSANDS) 80
7.3.3 EUROPE: REGULATIONS 80
7.3.3.1 General Data Protection Regulation 80
7.3.3.2 European Committee for Standardization 80
7.3.3.3 European Technical Standards Institute 81

7.4 ASIA PACIFIC 81
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 81
7.4.2 ASIA PACIFIC: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC) 82
FIGURE 22 ASIA PACIFIC: THE FEDERATED LEARNING SOLUTIONS MARKET,
2023–2028 (USD THOUSANDS) 82
7.4.3 ASIA PACIFIC: REGULATIONS 82
7.4.3.1 Privacy Commissioner for Personal Data 82
7.4.3.2 Act on the Protection of Personal Information 83
7.4.3.3 Critical Information Infrastructure 83
7.4.3.4 International Organization for Standardization 27001 83
7.4.3.5 Personal Data Protection Act 83
7.5 REST OF WORLD 84
7.5.1 STRENGTHENING OF NETWORK INFRASTRUCTURE, GROWING FOOTHOLD OF GLOBAL COMPANIES, AND INCREASING TECHNOLOGY ADOPTION TO DRIVE THE ADOPTION OF FEDERATED LEARNING 84
7.5.2 REST OF WORLD: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC) 85
FIGURE 23 REST OF WORLD: THE FEDERATED LEARNING SOLUTIONS MARKET, 2023–2028 (USD THOUSANDS) 85
7.5.3 MIDDLE EAST AND AFRICA: REGULATIONS 85
7.5.3.1 Israeli Privacy Protection Regulations (Data Security), 5777-2017 85
7.5.3.2 Cloud Computing Framework 86
7.5.3.3 GDPR Applicability in the Kingdom of Saudi Arabia (KSA) 86
7.5.3.4 Protection of Personal Information Act 86
7.5.4 LATIN AMERICA: REGULATIONS 86
7.5.4.1 Brazil Data Protection Law 86
7.5.4.2 Argentina Personal Data Protection Law No. 25.326 87
8 COMPANY PROFILES 88
8.1 INTRODUCTION 88
(Business Overview, Solutions, Key Insights, Recent Developments, MnM View)*
8.2 NVIDIA 88
TABLE 11 NVIDIA: BUSINESS OVERVIEW 89
FIGURE 24 NVIDIA: COMPANY SNAPSHOT 89
TABLE 12 NVIDIA: FEDERATED LEARNING SOLUTIONS MARKET: SOLUTION LAUNCHES AND ENHANCEMENTS 90
TABLE 13 NVIDIA: FEDERATED LEARNING SOLUTIONS MARKET: DEALS 91
FIGURE 25 BUSINESS MODEL CANVAS: NVIDIA 91
8.3 CLOUDERA 93
TABLE 14 CLOUDERA: BUSINESS OVERVIEW 93
FIGURE 26 CLOUDERA: COMPANY SNAPSHOT 94
TABLE 15 CLOUDERA: FEDERATED LEARNING SOLUTIONS MARKET: SOLUTION LAUNCHES AND ENHANCEMENTS 94
TABLE 16 CLOUDERA: FEDERATED LEARNING SOLUTIONS MARKET: DEALS 95
FIGURE 27 BUSINESS MODEL CANVAS: CLOUDERA 95
8.4 IBM 97
TABLE 17 IBM: BUSINESS OVERVIEW 97
FIGURE 28 IBM: COMPANY SNAPSHOT 98
TABLE 18 IBM: FEDERATED LEARNING SOLUTIONS MARKET: RESEARCH PROJECT 99
TABLE 19 IBM: FEDERATED LEARNING SOLUTIONS MARKET: DEALS 99
FIGURE 29 BUSINESS MODEL CANVAS: IBM 100
8.5 MICROSOFT 101
TABLE 20 MICROSOFT: BUSINESS OVERVIEW 101
FIGURE 30 MICROSOFT: COMPANY SNAPSHOT 102
TABLE 21 MICROSOFT: FEDERATED LEARNING SOLUTIONS MARKET:
RESEARCH PROJECT 103
TABLE 22 MICROSOFT: FEDERATED LEARNING SOLUTIONS MARKET:
SOLUTION LAUNCHES AND ENHANCEMENTS 103
TABLE 23 MICROSOFT: FEDERATED LEARNING SOLUTIONS MARKET: DEALS 103
FIGURE 31 BUSINESS MODEL CANVAS: MICROSOFT 104
8.6 GOOGLE 105
TABLE 24 GOOGLE: BUSINESS OVERVIEW 105
FIGURE 32 GOOGLE: COMPANY SNAPSHOT 106
TABLE 25 GOOGLE: FEDERATED LEARNING SOLUTIONS MARKET: RESEARCH PROJECT 107
TABLE 26 GOOGLE: FEDERATED LEARNING SOLUTIONS MARKET: SOLUTION LAUNCHES AND ENHANCEMENTS 107
FIGURE 33 BUSINESS MODEL CANVAS: GOOGLE 108
8.7 OWKIN 109
TABLE 27 OWKIN: FEDERATED LEARNING SOLUTIONS MARKET:
RESEARCH PROJECT AND FUNDING 110
TABLE 28 OWKIN: FEDERATED LEARNING SOLUTIONS MARKET: DEALS 111
8.8 INTELLEGENS 112
TABLE 29 INTELLEGENS: FEDERATED LEARNING SOLUTIONS MARKET:
RESEARCH PROJECT AND FUNDING 112
8.9 DATAFLEETS 114
TABLE 30 DATAFLEETS: FEDERATED LEARNING SOLUTIONS MARKET: RESEARCH PROJECT AND FUNDING 115
TABLE 31 DATAFLEETS: FEDERATED LEARNING SOLUTIONS MARKET: DEALS 115
8.10 EDGE DELTA 116
TABLE 32 EDGE DELTA: FEDERATED LEARNING SOLUTIONS MARKET: RESEARCH PROJECT AND FUNDING 116
TABLE 33 EDGE DELTA: FEDERATED LEARNING SOLUTIONS MARKET: DEALS 117
8.11 ENVEIL 118
TABLE 34 ENVEIL: FEDERATED LEARNING SOLUTIONS MARKET: RESEARCH PROJECT AND FUNDING 118
TABLE 35 ENVEIL: FEDERATED LEARNING SOLUTIONS MARKET: SOLUTION LAUNCHES AND ENHANCEMENTS 119

8.12 LIFEBIT 120
TABLE 36 LIFEBIT: FEDERATED LEARNING SOLUTIONS MARKET: RESEARCH PROJECT AND FUNDING 120
TABLE 37 LIFEBIT: FEDERATED LEARNING SOLUTIONS MARKET: SOLUTION LAUNCHES AND ENHANCEMENTS 121
8.13 SECURE AI LABS 122
8.14 SHERPA.AI 123
8.15 DECENTRALIZED MACHINE LEARNING 123
8.16 CONSILIENT 124
*Details on Business Overview, Solutions, Key Insights, Recent Developments, MnM View might not be captured in case of unlisted companies.
8.17 COMPETITIVE BENCHMARKING 125
TABLE 38 COMPETITIVE BENCHMARKING: OFFERINGS AND REGIONAL PRESENCE 125
TABLE 39 COMPETITIVE BENCHMARKING: TARGET VERTICALS 125
9 ADJACENT AND RELATED MARKETS 127
9.1 INTRODUCTION 127
9.2 MACHINE LEARNING MARKET – GLOBAL FORECAST TO 2022 127
9.2.1 MARKET DEFINITION 127
9.2.2 MARKET OVERVIEW 127
TABLE 40 GLOBAL MACHINE LEARNING MARKET SIZE AND GROWTH RATE,
2015–2022 (USD MILLION, Y-O-Y %) 127
9.2.2.1 Machine learning market, by vertical 128
TABLE 41 MACHINE LEARNING MARKET SIZE, BY VERTICAL,
2015–2022 (USD MILLION) 128
9.2.2.2 Machine learning market, by deployment mode 128
TABLE 42 MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE,
2015–2022 (USD MILLION) 128
9.2.2.3 Machine learning market, by organization size 129
TABLE 43 MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE,
2015–2022 (USD MILLION) 129
9.2.2.4 Machine learning market, by service 129
TABLE 44 MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION) 129
9.2.2.5 Machine learning market, by region 130
TABLE 45 MACHINE LEARNING MARKET SIZE, BY REGION, 2015–2022 (USD MILLION) 130
9.3 EDGE AI SOFTWARE MARKET – GLOBAL FORECAST TO 2026 130
9.3.1 MARKET DEFINITION 130
9.3.2 MARKET OVERVIEW 131
TABLE 46 GLOBAL EDGE AI SOFTWARE MARKET SIZE AND GROWTH RATE,
2014–2019 (USD MILLION, Y-O-Y%) 131
TABLE 47 GLOBAL EDGE AI SOFTWARE MARKET SIZE AND GROWTH RATE,
2019–2026 (USD MILLION, Y-O-Y%) 131

9.3.2.1 Edge AI software market, by component 132
TABLE 48 EDGE AI SOFTWARE MARKET SIZE, BY COMPONENT,
2014–2019 (USD MILLION) 132
TABLE 49 EDGE AI SOFTWARE MARKET SIZE, BY COMPONENT,
2019–2026 (USD MILLION) 132
9.3.2.2 Edge AI software market, by data source 132
TABLE 50 EDGE AI SOFTWARE MARKET SIZE, BY DATA SOURCE,
2014–2019 (USD MILLION) 133
TABLE 51 EDGE AI SOFTWARE MARKET SIZE, BY DATA SOURCE,
2019–2026 (USD MILLION) 133
9.3.2.3 Edge AI software market, by application 133
TABLE 52 EDGE AI SOFTWARE MARKET SIZE, BY APPLICATION,
2014–2019 (USD MILLION) 134
TABLE 53 EDGE AI SOFTWARE MARKET SIZE, BY APPLICATION,
2019–2026 (USD MILLION) 134
9.3.2.4 Edge AI software market, by vertical 135
TABLE 54 EDGE AI SOFTWARE MARKET SIZE, BY VERTICAL, 2014–2019 (USD MILLION) 135
TABLE 55 EDGE AI SOFTWARE MARKET SIZE, BY VERTICAL, 2019–2026 (USD MILLION) 135
9.3.2.5 Edge AI software market, by region 136
TABLE 56 EDGE AI SOFTWARE MARKET SIZE, BY REGION, 2014–2019 (USD MILLION) 136
TABLE 57 EDGE AI SOFTWARE MARKET SIZE, BY REGION, 2019–2026 (USD MILLION) 136
10 APPENDIX 137
10.1 INDUSTRY EXPERTS 137
10.2 DISCUSSION GUIDE 138
10.3 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 142
10.4 AVAILABLE CUSTOMIZATIONS 144
10.5 RELATED REPORTS 144
10.6 AUTHOR DETAILS 145



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