연합 학습 시장 : 2028년까지의 세계 예측

출판 : MarketsandMarkets(마켓츠앤드마켓츠)출판년월 : 2022년04월

연합 학습 시장 : 용도(창약, 산업용 IoT, 리스크 관리), 수직(헬스케어 & 라이프 사이언스, BFSI, 제조, 자동차 및 수송, 에너지 및 공익 사업), 지역별 – 2028년까지의 세계 예측

Federated Learning Market by Application (Drug Discovery, Industrial IoT, Risk Management), Vertical (Healthcare and Life Sciences, BFSI, Manufacturing, Automotive and Transportation, Energy and Utilities) and Region – Global Forecast to 2028

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As per AS-IS scenario, the global federated learning market size to grow from USD 127 million in 2023 to USD 210 million by 2028, at a Compound Annual Growth Rate (CAGR) of 10.6% during the forecast period. The major factors including the ability to support enterprises to collaborate on a common machine learning (ML) prototype by keeping information on machines and the power to control predictive features on connected devices without affecting user experience or leaking private information are expected to drive the growth for federated learning solutions.
AS-IS 시나리오에 따르면 글로벌 연합 학습 시장 규모는 예측 기간 동안 10.6%의 CAGR(연간 복합 성장률)로 2023년 1억 2,700만 달러에서 2028년 2억 1,000만 달러로 성장할 것입니다. 기업이 기계에 대한 정보를 유지하여 공통 기계 학습(ML) 프로토타입에 대해 협업할 수 있도록 지원하는 능력과 사용자 경험에 영향을 미치거나 개인 정보를 누출하지 않고 연결된 장치의 예측 기능을 제어하는 ​​능력을 포함한 주요 요인이 성장을 주도할 것으로 예상됩니다.

As per AS-IS scenario, among verticals, the automotive and transportation segment to grow at a the highest CAGR during the forecast period
The federated learning solutions market is segmented on verticals into BFSI, healthcare and life sciences, retail and eCommerce, energy and utilities, and manufacturing, automotive and transportation, IT and telecommunications and other verticals (government, and media and entertainment). As per AS-IS scenario, the automotive and transportation vertical is expected to grow at the highest CAGR during the forecast period. With the introduction of automated vehicles, the focus was on data, edge-to-edge computer technology handling, and improved ML algorithm in addition to making automated vehicles reliable and secure for seamless integration through one area of the globe to another, even as analyzing information and personal confidentiality wirelessly. Effective learning chooses the most relevant pieces of data to classify and add to the instructional pool. Furthermore, they can use federated learning to retrain the network across numerous devices in a decentralized manner using the specific information that we will receive from every car to identify these imperfections and assist in preventing the car from hitting other potholes.
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 market in APAC is projected to grow at the highest CAGR from 2023 to 2028. APAC is witnessing an advanced and dynamic adoption of new technologies. Key countries such as India, Japan, Singapore, and China are focusing on implementing regulations for data privacy and security in the coming years. This would create an opportunity to implement federated learning solutions for the security and privacy of data. Many Asian countries are leveraging information-intensive big data technologies and AI to collect data from various data sources. The commercialization of big data, AI, and IoT technologies and the need for further advancements to leverage these technologies to the best is expected to increase adoption in the future.

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 market.
 By Company: Tier I: 35%, Tier II: 45%, and Tier III: 20%
 By Designation: C-Level Executives: 35%, D-Level Executives: 25%, and Managers: 40%
 By Region: APAC: 25%, Europe: 30%, North America: 30%, MEA: 10%, Latin America: 5%
The report includes the study of key players offering federated learning solutions and services. It profiles major vendors in the federated learning market. The major players in the federated learning market include NVIDIA (US), Cloudera (US), IBM (US), Microsoft (US), Google (US), Intel(US), Owkin(US), Intellegens(UK), Edge Delta(US), Enveil(US), Lifebit(UK), DataFleets(US), Secure AI Labs(US), and Sherpa.AI(Spain).
Research Coverage
The market study covers the federated learning market across segments. It aims at estimating the market size and the growth potential of this market across different segments, such as application, vertical, and region. 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, 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 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.


목차

TABLE OF CONTENTS

1 INTRODUCTION (Page No. – 21)
1.1 OBJECTIVES OF THE STUDY
1.2 MARKET DEFINITION
1.2.1 INCLUSIONS AND EXCLUSIONS
1.3 MARKET SCOPE
1.3.1 MARKET SEGMENTATION
1.3.2 YEARS CONSIDERED FOR THE STUDY
1.4 CURRENCY CONSIDERED
TABLE 1 UNITED STATES DOLLAR EXCHANGE RATE, 2018–2021
1.5 STAKEHOLDERS
1.6 SUMMARY OF CHANGES

2 RESEARCH METHODOLOGY (Page No. – 25)
FIGURE 1 FEDERATED LEARNING MARKET: RESEARCH DESIGN
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
TABLE 2 PRIMARY INTERVIEWS
2.1.2.1 Breakup of primary profiles
2.1.2.2 Key industry insights
2.2 MARKET BREAKUP AND DATA TRIANGULATION
FIGURE 2 DATA TRIANGULATION
2.3 MARKET SIZE ESTIMATION
FIGURE 3 MARKET: MARKET ESTIMATION APPROACH
2.4 MARKET FORECAST
TABLE 3 CRITICAL FACTORS IMPACTING THE MARKET GROWTH
2.5 ASSUMPTIONS FOR THE STUDY
2.6 LIMITATIONS OF THE STUDY

3 EXECUTIVE SUMMARY (Page No. – 35)
3.1 FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 4 GLOBAL FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSAND)
FIGURE 5 HEALTHCARE AND LIFE SCIENCES VERTICAL TO HOLD THE LARGEST MARKET SHARE DURING THE FORECAST PERIOD
FIGURE 6 EUROPE TO HOLD THE LARGEST MARKET SHARE BY 2023
3.2 SUMMARY OF KEY FINDINGS

4 MARKET OVERVIEW AND INDUSTRY TRENDS (Page No. – 40)
4.1 INTRODUCTION
4.2 FEDERATED LEARNING: EVOLUTION
FIGURE 7 EVOLUTION OF THE MARKET
4.3 FEDERATED LEARNING: TYPES
FIGURE 8 TYPES OF FEDERATED LEARNING
4.4 FEDERATED LEARNING: ARCHITECTURE
FIGURE 9 ARCHITECTURE OF FEDERATED LEARNING
4.5 MARKET DYNAMICS
FIGURE 10 DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES: FEDERATED LEARNING MARKET
4.5.1 DRIVERS
4.5.1.1 Growing need to increase learning between devices and organizations
4.5.1.2 Ability to ensure better data privacy and security by training algorithms on decentralized devices
4.5.1.3 Growing adoption of federated learning in various applications for data privacy
4.5.1.4 Ability of federated learning to address the difficulty of safeguarding individuals’ anonymity
4.5.2 RESTRAINTS
4.5.2.1 Lack of skilled technical expertise
4.5.3 OPPORTUNITIES
4.5.3.1 Federated learning enables distributed participants to collaboratively learn a commonly shared model while holding data locally
4.5.3.2 Capability to enable predictive features on smart devices without impacting the user experience and leaking private information
4.5.4 CHALLENGES
4.5.4.1 Issues of high latency and communication inefficiency
4.5.4.2 System integration and interoperability issue
4.5.4.3 Indirect information leakage
4.6 IMPACT OF DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES ON THE FEDERATED LEARNING MARKET
4.7 ARTIFICIAL INTELLIGENCE: ECOSYSTEM
FIGURE 11 ARTIFICIAL INTELLIGENCE ECOSYSTEM
4.8 USE CASE ANALYSIS
4.8.1 WEBANK AND A CAR RENTAL SERVICE PROVIDER ENABLE INSURANCE INDUSTRY TO REDUCE DATA TRAFFIC VIOLATIONS THROUGH FEDERATED LEARNING
4.8.2 FEDERATED LEARNING ENABLE HEALTHCARE COMPANIES TO ENCRYPT AND PROTECT PATIENT’S DATA
4.8.3 WEBANK AND EXTREME VISION INTRODUCED ONLINE VISUAL OBJECT DETECTION PLATFORM POWERED BY FEDERATED LEARNING TO STORE DATA IN CLOUD
4.8.4 WEBANK INTRODUCED FEDERATED LEARNING MODEL FOR ANTI-MONEY LAUNDERING
4.8.5 INTELLEGENS SOLUTION ADOPTION MAY HELP CLINICALS ANALYZE HEART RATE DATA
4.9 SUPPLY CHAIN ANALYSIS
FIGURE 12 SUPPLY CHAIN ANALYSIS
4.10 PATENT ANALYSIS
4.10.1 METHODOLOGY
4.10.2 DOCUMENT TYPE
TABLE 4 PATENTS FILED
4.10.3 INNOVATION AND PATENT APPLICATIONS
FIGURE 13 TOTAL NUMBER OF PATENTS GRANTED IN A YEAR, 2015–2021
4.10.3.1 Top applicants
FIGURE 14 TOP TEN COMPANIES WITH THE HIGHEST NUMBER OF PATENT APPLICATIONS, 2015–2021
TABLE 5 TOP EIGHT PATENT OWNERS (US) IN THE FEDERATED LEARNING MARKET, 2015–2021
4.11 TECHNOLOGY ANALYSIS
4.11.1 FEDERATED LEARNING VS DISTRIBUTED MACHINE LEARNING
4.11.2 FEDERATED LEARNING VS EDGE COMPUTING
4.11.3 FEDERATED LEARNING VS FEDERATED DATABASE SYSTEMS
4.11.4 FEDERATED LEARNING VS SWARM LEARNING
4.12 RESEARCH PROJECTS: FEDERATED LEARNING
4.12.1 MACHINE LEARNING LEDGER ORCHESTRATION FOR DRUG DISCOVERY (MELLODDY)
4.12.1.1 Participants
4.12.2 FEDAI
4.12.3 PADDLEPADDLE
4.12.4 FEATURECLOUD
4.12.5 MUSKETEER PROJECT
4.13 REGULATORY LANDSCAPE
4.13.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 6 NORTH AMERICA: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 7 EUROPE: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 8 ASIA PACIFIC: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
TABLE 9 REST OF WORLD: LIST OF REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
4.13.2 REGULATORY IMPLICATIONS AND INDUSTRY STANDARDS
4.13.3 GENERAL DATA PROTECTION REGULATION
4.13.4 SEC RULE 17A-4
4.13.5 ISO/IEC 27001
4.13.6 SYSTEM AND ORGANIZATION CONTROLS 2 TYPE II COMPLIANCE
4.13.7 FINANCIAL INDUSTRY REGULATORY AUTHORITY
4.13.8 FREEDOM OF INFORMATION ACT
4.13.9 HEALTH INSURANCE PORTABILITY AND ACCOUNTABILITY ACT PLAY
4.14 KEY CONFERENCES AND EVENTS IN 2022
TABLE 10 FEDERATED LEARNING MARKET: DETAILED LIST OF CONFERENCES AND EVENTS
4.15 KEY STAKEHOLDERS AND BUYING CRITERIA
4.15.1 KEY STAKEHOLDERS IN THE BUYING PROCESS
FIGURE 15 INFLUENCE OF STAKEHOLDERS IN THE BUYING PROCESS FOR TOP VERTICALS
TABLE 11 INFLUENCE OF STAKEHOLDERS IN THE BUYING PROCESS FOR TOP VERTICALS (%)
TABLE 12 BUYING PROCESS FOR TOP VERTICALS
4.15.2 BUYING CRITERIA
FIGURE 16 KEY BUYING CRITERIA FOR TOP THREE VERTICALS
TABLE 13 KEY BUYING CRITERIA FOR TOP THREE VERTICALS
4.16 TRENDS/DISRUPTIONS IMPACTING BUYERS
FIGURE 17 MARKET: TRENDS/DISRUPTIONS IMPACTING BUYERS

5 FEDERATED LEARNING MARKET, BY APPLICATION (Page No. – 75)
5.1 INTRODUCTION
5.2 DRUG DISCOVERY
5.2.1 ABILITY TO ACCELERATE DRUG DISCOVERY BY ENABLING INCREASED COLLABORATIONS FOR FASTER TREATMENT TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
5.2.2 ASSURANCE OF DATA PRIVACY IS CREATING OPPORTUNITIES FOR FEDERATED LEARNING
5.3 SHOPPING EXPERIENCE PERSONALIZATION
5.3.1 GROWING FOCUS ON ENABLING PERSONALIZED SHOPPING EXPERIENCE WHILE ENSURING CUSTOMER DATA PRIVACY AND NETWORK TRAFFIC REDUCTION TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
5.3.2 USE OF FEDERATED LEARNING IN PERSONALIZED RECOMMENDATION
5.4 DATA PRIVACY AND SECURITY MANAGEMENT
5.4.1 FEDERATED LEARNING SOLUTIONS ENABLE BETTER DATA PRIVACY AND SECURITY MANAGEMENT BY LIMITING THE NEED TO MOVE DATA ACROSS NETWORKS BY TRAINING ALGORITHM
5.4.2 FEDERATED LEARNING HAS EMERGED AS A SOLUTION FOR FACILITATING REMOTE GROUP WORK WHILE KEEPING THE LEARNING DATA PRIVATE
5.5 RISK MANAGEMENT
5.5.1 ABILITY TO ENABLE BFSI ORGANIZATIONS TO COLLABORATE AND LEARN A SHARED PREDICTION MODEL WITHOUT SHARING DATA AND PERFORM EFFICIENT CREDIT RISK ASSESSMENT TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
5.5.2 FEDERATED MACHINE LEARNING FOR LOAN RISK PREDICTION
5.6 INDUSTRIAL INTERNET OF THINGS
5.6.1 FEDERATED LEARNING SOLUTIONS ENABLE PREDICTIVE MAINTENANCE ON EDGE DEVICES WITHOUT CENTRALIZING DATA
5.6.2 BLOCKCHAIN BASED FEDERATED LEARNING SOLUTIONS HELPS IN DEVICE RECOGNITION IN IIOT
5.7 ONLINE VISUAL OBJECT DETECTION
5.7.1 ABILITY TO ENABLE SAFETY MONITORING BY ENHANCED ONLINE VISUAL OBJECT DETECTION FOR SMART CITY APPLICATIONS TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
5.7.2 FEDCV A FRAMEWORK FOR DIVERSE COMPUTER VISION TASKS
5.8 AUGMENTED REALITY/VIRTUAL REALITY
5.8.1 OUTPUT SECURITY FOR MULTI-USER AUGMENTED REALITY USING FEDERATED REINFORCEMENT LEARNING
5.9 OTHER APPLICATIONS

6 FEDERATED LEARNING MARKET, BY VERTICAL (Page No. – 84)
6.1 INTRODUCTION
TABLE 14 PESSIMISTIC SCENARIO: MARKET SIZE, BY VERTICAL, 2023–2028 (USD THOUSANDS)
TABLE 15 AS-IS SCENARIO: MARKET SIZE, BY VERTICAL, 2023–2028 (USD THOUSANDS)
TABLE 16 OPTIMISTIC SCENARIO: MARKET SIZE, BY VERTICAL, 2023–2028 (USD THOUSANDS)
6.2 BANKING, FINANCIAL SERVICES, AND INSURANCE
6.2.1 ABILITY TO REDUCE MALICIOUS ACTIVITIES AND PROTECT CUSTOMER DATA TO DRIVE THE ADOPTION OF FEDERATED LEARNING SOLUTIONS IN THE BFSI VERTICAL
6.2.2 BANKING, FINANCIAL SERVICES, AND INSURANCE: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 18 BANKING, FINANCIAL SERVICES, AND INSURANCE: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
6.3 HEALTHCARE AND LIFE SCIENCES
6.3.1 LARGE POOL OF APPLICATIONS, MULTIPLE RESEARCH INITIATIVES, AND COLLABORATIONS AMONG TECHNOLOGY VENDORS AND HEALTHCARE AND LIFE SCIENCES ORGANIZATIONS TO DRIVE MARKET GROWTH
6.3.2 HEALTHCARE AND LIFE SCIENCES: FORECAST 2023–2028 (OPTIMISTIC/ AS-IS/PESSIMISTIC)
FIGURE 19 HEALTHCARE AND LIFE SCIENCES: MARKET, 2023–2028 (USD THOUSANDS)
6.4 RETAIL AND ECOMMERCE
6.4.1 ABILITY TO ENABLE PERSONALIZED CUSTOMER EXPERIENCES WHILE ENSURING CUSTOMER DATA PRIVACY TO DRIVE THE ADOPTION OF FEDERATED LEARNING IN THE RETAIL AND ECOMMERCE VERTICAL
6.4.2 RETAIL AND ECOMMERCE: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 20 RETAIL AND ECOMMERCE: THE FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
6.5 MANUFACTURING
6.5.1 FOCUS ON SMART MANUFACTURING AND NEED FOR ENHANCED OPERATIONAL INTELLIGENCE TO DRIVE THE ADOPTION OF FEDERATED LEARNING ACROSS THE MANUFACTURING VERTICAL
6.5.2 MANUFACTURING: FORECAST 2023–2028 (OPTIMISTIC/ AS-IS/PESSIMISTIC)
FIGURE 21 MANUFACTURING: THE MARKET, 2023–2028 (USD THOUSANDS)
6.6 ENERGY AND UTILITIES
6.6.1 NEED TO CONTROL CYBERATTACKS AND IMPROVE POWER GRID RESILIENCE TO DRIVE THE ADOPTION OF FEDERATED LEARNING IN THE ENERGY AND UTILITIES VERTICAL
6.6.2 ENERGY AND UTILITIES: FORECAST 2023–2028 (OPTIMISTIC/ AS-IS/PESSIMISTIC)
FIGURE 22 ENERGY AND UTILITIES: THE FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
6.7 AUTOMOTIVE AND TRANSPORTATION
6.7.1 FEDERATED LEARNING TO RETRAIN THE NETWORK ACROSS NUMEROUS DEVICES IN A DECENTRALIZED MANNER
6.7.2 AUTOMOTIVE AND TRANSPORTATION: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 23 AUTOMOTIVE AND TRANSPORTATION: THE MARKET, 2023–2028 (USD THOUSANDS)
6.8 IT AND TELECOMMUNICATION
6.8.1 TRANSFER OF DATA RAISES PRIVACY CONCERNS CAUSING SAFETY AND ECONOMIC DIFFICULTIES
6.8.2 IT AND TELECOMMUNICATION: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 24 IT AND TELECOMMUNICATION: THE MARKET, 2023–2028 (USD THOUSANDS)
6.9 OTHER VERTICALS
FIGURE 25 OTHER VERTICALS: THE MARKET, 2023–2028 (USD THOUSANDS)

7 FEDERATED LEARNING MARKET, BY REGION (Page No. – 99)
7.1 INTRODUCTION
TABLE 17 PESSIMISTIC SCENARIO: MARKET SIZE, BY REGION, 2023–2028 (USD THOUSANDS)
TABLE 18 AS-IS SCENARIO: MARKET SIZE, BY REGION, 2023–2028 (USD THOUSANDS)
TABLE 19 OPTIMISTIC SCENARIO: MARKET SIZE, BY REGION, 2023–2028 (USD THOUSANDS)
7.2 NORTH AMERICA
7.2.1 HIGH FOCUS OF NORTH AMERICAN COMPANIES TOWARD RESEARCH IN FEDERATED LEARNING TO ENABLE FUTURISTIC DATA-TRAINED MODELS
7.2.2 NORTH AMERICA: MARKET DRIVERS
7.2.3 NORTH AMERICA: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 26 NORTH AMERICA: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
7.2.4 NORTH AMERICA: REGULATIONS
7.2.4.1 Health Insurance Portability and Accountability Act of 1996
7.2.4.2 California Consumer Privacy Act
7.2.4.3 Gramm–Leach–Bliley Act
7.2.4.4 Health Information Technology for Economic and Clinical Health Act
7.2.4.5 Federal Information Security Management Act
7.2.4.6 Payment Card Industry Data Security Standard
7.2.4.7 Federal Information Processing Standards
7.2.4.8 Sarbanes Oxley Act
7.2.4.9 United States Securities and Exchange Commission
7.3 EUROPE
7.3.1 HIGH FOCUS ON DATA PRIVACY AND COMPLIANCE, AND INCREASED RESEARCH COLLABORATIONS TO DRIVE THE ADOPTION OF FEDERATED LEARNING IN EUROPE
7.3.2 EUROPE: FEDERATED LEARNING MARKET DRIVERS
7.3.3 EUROPE: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 27 EUROPE: MARKET, 2023–2028 (USD THOUSANDS)
7.3.4 EUROPE: REGULATIONS
7.3.4.1 General Data Protection Regulation
7.3.4.2 European Committee for Standardization
7.3.4.3 European Technical Standards Institute
7.3.4.4 European Market Infrastructure Regulation
7.4 ASIA PACIFIC
7.4.1 COUNTRY-WISE FOCUS ON DATA PRIVACY REGULATIONS ALONG WITH THE INCREASING ADOPTION OF EDGE AI AND THE NEED FOR PERSONALIZED SERVICES TO SPUR THE ADOPTION OF FEDERATED LEARNING SOLUTIONS
7.4.2 ASIA PACIFIC: MARKET DRIVERS
7.4.3 ASIA PACIFIC: FORECAST 2023–2028 (OPTIMISTIC/AS-IS /PESSIMISTIC)
FIGURE 28 ASIA PACIFIC: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
7.4.4 ASIA PACIFIC: REGULATIONS
7.4.4.1 Privacy Commissioner for Personal Data
7.4.4.2 Act on the Protection of Personal Information
7.4.4.3 Critical information infrastructure
7.4.4.4 International organization for standardization 27001
7.4.4.5 Personal data protection act
7.5 MIDDLE EAST AND AFRICA
7.5.1 STRENGTHENING OF NETWORK INFRASTRUCTURE, GROWING FOOTHOLD OF GLOBAL COMPANIES, AND INCREASING TECHNOLOGY ADOPTION TO DRIVE THE ADOPTION OF FEDERATED LEARNING
7.5.2 MIDDLE EAST AND AFRICA: MARKET DRIVERS
7.5.3 MIDDLE EAST AND AFRICA: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/PESSIMISTIC)
FIGURE 29 MIDDLE EAST AND AFRICA: FEDERATED LEARNING MARKET, 2023–2028 (USD THOUSANDS)
7.5.4 MIDDLE EAST AND AFRICA: REGULATIONS
7.5.4.1 Israeli Privacy Protection Regulations (Data Security), 5777-2017
7.5.4.2 Cloud Computing Framework
7.5.4.3 GDPR applicability in the Kingdom of Saudi Arabia
7.5.4.4 Protection of Personal Information Act
7.6 LATIN AMERICA
7.6.1 GROWING ADOPTION OF AI TECHNOLOGY TO DRIVE THE FEDERATED LEARNING MARKET
7.6.2 LATIN AMERICA: MARKET DRIVERS
7.6.3 LATIN AMERICA: FORECAST 2023–2028 (OPTIMISTIC/AS-IS/ PESSIMISTIC)
FIGURE 30 LATIN AMERICA: MARKET, 2023–2028 (USD THOUSANDS)
7.6.4 LATIN AMERICA: REGULATIONS
7.6.4.1 Brazil Data Protection Law
7.6.4.2 Argentina Personal Data Protection Law No. 25.326
7.6.4.3 Federal Law on Protection of Personal Data Held by Individuals

8 COMPETITIVE LANDSCAPE (Page No. – 117)
8.1 INTRODUCTION
FIGURE 31 MARKET EVALUATION FRAMEWORK
8.2 KEY PLAYER STRATEGIES/RIGHT TO WIN
8.2.1 OVERVIEW OF STRATEGIES ADOPTED BY KEY FEDERATED LEARNING VENDORS
8.3 HISTORICAL REVENUE ANALYSIS OF TOP VENDORS
FIGURE 32 HISTORICAL REVENUE ANALYSIS
8.4 COMPETITIVE BENCHMARKING
TABLE 20 MARKET: NEW LAUNCHES, 2019–2022
TABLE 21 FEDERATED LEARNING MARKET: DEALS, 2019–2022

9 COMPANY PROFILES (Page No. – 126)
(Business Overview, Products Offered, Recent Developments, MnM View Right to win, Strategic choices made, Weaknesses and competitive threats) *
9.1 INTRODUCTION
9.2 KEY PLAYERS
9.2.1 NVIDIA
TABLE 22 NVIDIA: BUSINESS OVERVIEW
FIGURE 33 NVIDIA: COMPANY SNAPSHOT
TABLE 23 NVIDIA: SOLUTIONS OFFERED
TABLE 24 NVIDIA: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 25 NVIDIA: DEALS
FIGURE 34 BUSINESS MODEL CANVAS: NVIDIA
9.2.2 GOOGLE
TABLE 26 GOOGLE: BUSINESS OVERVIEW
FIGURE 35 GOOGLE: COMPANY SNAPSHOT
TABLE 27 GOOGLE: SOLUTIONS OFFERED
TABLE 28 GOOGLE: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 29 GOOGLE: OTHERS
FIGURE 36 BUSINESS MODEL CANVAS: GOOGLE
9.2.3 MICROSOFT
TABLE 30 MICROSOFT: BUSINESS OVERVIEW
FIGURE 37 MICROSOFT: COMPANY SNAPSHOT
TABLE 31 MICROSOFT: SOLUTIONS OFFERED
TABLE 32 MICROSOFT: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 33 MICROSOFT: DEALS
TABLE 34 MICROSOFT: OTHERS
FIGURE 38 BUSINESS MODEL CANVAS: MICROSOFT
9.2.4 IBM
TABLE 35 IBM: BUSINESS OVERVIEW
FIGURE 39 IBM: COMPANY SNAPSHOT
TABLE 36 IBM: SOLUTIONS OFFERED
TABLE 37 IBM: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 38 IBM: DEALS
FIGURE 40 BUSINESS MODEL CANVAS: IBM
9.2.5 CLOUDERA
TABLE 39 CLOUDERA: BUSINESS OVERVIEW
FIGURE 41 CLOUDERA: COMPANY SNAPSHOT
TABLE 40 CLOUDERA: SOLUTIONS OFFERED
TABLE 41 CLOUDERA: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 42 CLOUDERA: DEALS
FIGURE 42 BUSINESS MODEL CANVAS: CLOUDERA
9.2.6 INTEL
TABLE 43 INTEL: BUSINESS OVERVIEW
FIGURE 43 INTEL: COMPANY SNAPSHOT
TABLE 44 INTEL: SOLUTIONS OFFERED
TABLE 45 INTEL: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 46 INTEL: DEALS
TABLE 47 INTEL: OTHERS
9.2.7 OWKIN
TABLE 48 OWKIN: BUSINESS OVERVIEW
TABLE 49 OWKIN: SOLUTIONS OFFERED
TABLE 50 OWKIN: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 51 OWKIN: DEALS
TABLE 52 OWKIN: OTHERS
9.2.8 INTELLEGENS
TABLE 53 INTELLEGENS: BUSINESS OVERVIEW
TABLE 54 INTELLEGENS: SOLUTIONS OFFERED
TABLE 55 INTELLEGENS: DEALS
TABLE 56 INELLEGENS: OTHERS
9.2.9 EDGE DELTA
TABLE 57 EDGE DELTA: BUSINESS OVERVIEW
TABLE 58 EDGE DELTA: SOLUTIONS OFFERED
TABLE 59 EDGE DELTA: DEALS
TABLE 60 EDGE DELTA: OTHERS
9.2.10 ENVEIL
TABLE 61 ENVEIL: BUSINESS OVERVIEW
TABLE 62 ENVEIL: SOLUTIONS OFFERED
TABLE 63 ENVEIL: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 64 ENVEIL: OTHERS
9.2.11 LIFEBIT
TABLE 65 LIFEBIT: BUSINESS OVERVIEW
TABLE 66 LIFEBIT: SOLUTIONS OFFERED
TABLE 67 LIFEBIT: PRODUCT LAUNCHES AND ENHANCEMENTS
TABLE 68 LIFEBIT: DEALS
TABLE 69 LIFEBIT: OTHERS
9.2.12 DATAFLEETS
TABLE 70 DATAFLEETS: BUSINESS OVERVIEW
TABLE 71 DATAFLEETS: SOLUTIONS OFFERED
TABLE 72 DATAFLEETS: DEALS
TABLE 73 DATAFLEETS: OTHERS
9.3 OTHERS KEY PLAYERS
9.3.1 SECURE AI LABS
9.3.2 SHERPA.AI
9.3.3 DECENTRALIZED MACHINE LEARNING
9.3.4 CONSILIENT
9.3.5 APHERIS
9.3.6 ACURATIO
9.3.7 FEDML
*Details on Business Overview, Products Offered, Recent Developments, MnM View, Right to win, Strategic choices made, Weaknesses and competitive threats might not be captured in case of unlisted companies.

10 ADJACENT AND RELATED MARKETS (Page No. – 175)
10.1 INTRODUCTION
10.1.1 RELATED MARKETS
10.1.2 LIMITATIONS
10.2 ARTIFICIAL INTELLIGENCE MARKET – GLOBAL FORECAST TO 2026
10.2.1 MARKET DEFINITION
10.2.2 MARKET OVERVIEW
TABLE 74 ARTIFICIAL INTELLIGENCE MARKET SIZE AND GROWTH RATE, 2021–2026 (USD BILLION, Y-O-Y%)
10.2.2.1 Artificial intelligence market, by vertical
TABLE 75 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY VERTICAL, 2021–2026 (USD BILLION)
10.2.2.2 Artificial intelligence market, by deployment mode
TABLE 76 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY DEPLOYMENT MODE, 2021–2026 (USD BILLION)
10.2.2.3 Machine learning market, by organization size
TABLE 77 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY ORGANIZATION SIZE, 2021–2026 (USD BILLION)
10.2.2.4 Artificial intelligence market, by service
TABLE 78 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY SERVICE, 2021–2026 (USD BILLION)
10.2.2.5 Artificial intelligence market, by region
TABLE 79 ARTIFICIAL INTELLIGENCE MARKET SIZE, BY REGION, 2021–2026 (USD BILLION)
10.3 MACHINE LEARNING MARKET – GLOBAL FORECAST TO 2022
10.3.1 MARKET DEFINITION
10.3.2 MARKET OVERVIEW
TABLE 80 GLOBAL MACHINE LEARNING MARKET SIZE AND GROWTH RATE, 2015–2022 (USD MILLION, Y-O-Y %)
10.3.2.1 Machine learning market, by vertical
TABLE 81 MACHINE LEARNING MARKET SIZE, BY VERTICAL, 2015–2022 (USD MILLION)
10.3.2.2 Machine learning market, by deployment mode
TABLE 82 MACHINE LEARNING MARKET SIZE, BY DEPLOYMENT MODE, 2015–2022 (USD MILLION)
10.3.2.3 Machine learning market, by organization size
TABLE 83 MACHINE LEARNING MARKET SIZE, BY ORGANIZATION SIZE, 2015–2022 (USD MILLION)
10.3.2.4 Machine learning market, by service
TABLE 84 MACHINE LEARNING MARKET SIZE, BY SERVICE, 2015–2022 (USD MILLION)
10.3.2.5 Machine learning market, by region
TABLE 85 MACHINE LEARNING MARKET SIZE, BY REGION, 2015–2022 (USD MILLION)
10.4 EDGE AI SOFTWARE MARKET – GLOBAL FORECAST TO 2026
10.4.1 MARKET DEFINITION
10.4.2 MARKET OVERVIEW
TABLE 86 GLOBAL EDGE AI SOFTWARE MARKET SIZE AND GROWTH RATE, 2014–2019 (USD MILLION, Y-O-Y%)
TABLE 87 GLOBAL EDGE AI SOFTWARE MARKET SIZE AND GROWTH RATE, 2019–2026 (USD MILLION, Y-O-Y%)
10.4.2.1 Edge AI software market, by component
TABLE 88 EDGE AI SOFTWARE MARKET SIZE, BY COMPONENT, 2014–2019 (USD MILLION)
TABLE 89 EDGE AI SOFTWARE MARKET SIZE, BY COMPONENT, 2019–2026 (USD MILLION)
10.4.2.2 Edge AI software market, by data source
TABLE 90 EDGE AI SOFTWARE MARKET SIZE, BY DATA SOURCE, 2014–2019 (USD MILLION)
TABLE 91 EDGE AI SOFTWARE MARKET SIZE, BY DATA SOURCE, 2019–2026 (USD MILLION)
10.4.2.3 Edge AI software market, by application
TABLE 92 EDGE AI SOFTWARE MARKET SIZE, BY APPLICATION, 2014–2019 (USD MILLION)
TABLE 93 EDGE AI SOFTWARE MARKET SIZE, BY APPLICATION, 2019–2026 (USD MILLION)
10.4.2.4 Edge AI software market, by vertical
TABLE 94 EDGE AI SOFTWARE MARKET SIZE, BY VERTICAL, 2014–2019 (USD MILLION)
TABLE 95 EDGE AI SOFTWARE MARKET SIZE, BY VERTICAL, 2019–2026 (USD MILLION)
10.4.2.5 Edge AI software market, by region
TABLE 96 EDGE AI SOFTWARE MARKET SIZE, BY REGION, 2014–2019 (USD MILLION)
TABLE 97 EDGE AI SOFTWARE MARKET SIZE, BY REGION, 2019–2026 (USD MILLION)

11 APPENDIX (Page No. – 189)
11.1 DISCUSSION GUIDE
11.2 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
11.3 AVAILABLE CUSTOMIZATIONS
11.4 RELATED REPORTS
11.5 AUTHOR DETAILS


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