Federated Learning for Industrial IOT Market Research Report - Segmentation by Component (Hardware, Software solutions, services), by Organization size (Large Enterprises, SMEs) and by Region- Size, Share, Growth Analysis | Forecast (2024 – 2030)
Published: 2024 - January
Report Code: IM-13028
Format:
Region: Global
Chapter 1. FEDERATED LEARNING FOR INDUSTRIAL IOT MARKET – Scope & Methodology
1.1. Market Segmentation
1.2. Assumptions
1.3. Research Methodology
1.4. Primary Sources
1.5. Secondary Sources
Chapter 2. FEDERATED LEARNING FOR INDUSTRIAL IOT MARKET – Executive Summary
2.1. Market Size & Forecast – (2023 – 2030) ($M/$Bn)
2.2. Key Trends & Insights
2.3. COVID-19 Impact Analysis
2.3.1. Impact during 2023 – 2030
2.3.2. Impact on Supply – Demand
Chapter 3. FEDERATED LEARNING FOR INDUSTRIAL IOT MARKET – Competition Scenario
3.1. Market Share Analysis
3.2. Product Benchmarking
3.3. Competitive Strategy & Development Scenario
3.4. Competitive Pricing Analysis
3.5. Supplier - Distributor Analysis
Chapter 4. FEDERATED LEARNING FOR INDUSTRIAL IOT MARKET - Entry Scenario
4.1. Case Studies – Start-up/Thriving Companies
4.2. Regulatory Scenario - By Region
4.3 Customer Analysis
4.4. Porter's Five Force Model
4.4.1. Bargaining Power of Suppliers
4.4.2. Bargaining Powers of Customers
4.4.3. Threat of New Entrants
4.4.4. Rivalry among Existing Players
4.4.5. Threat of Substitutes
Chapter 5. FEDERATED LEARNING FOR INDUSTRIAL IOT MARKET - Landscape
5.1. Value Chain Analysis – Key Stakeholders Impact Analysis
5.2. Market Drivers
5.3. Market Restraints/Challenges
5.4. Market Opportunities
Chapter 6. FEDERATED LEARNING FOR INDUSTRIAL IOT MARKET –By Verticals
6.1. Banking, Financial Services, & Insurance
6.2. Healthcare & Life Sciences
6.3. Retail & E-commerce
6.4. Manufacturing
6.5. Energy & Utilities
6.6. Automotive & Transportation
6.7. IT & Telecommunication
6.8. Others
Chapter 7. FEDERATED LEARNING FOR INDUSTRIAL IOT MARKET – By Region
7.1. North America
7.2. Europe
7.3.The Asia Pacific
7.4.Latin America
7.5. Middle-East and Africa
Chapter 8. FEDERATED LEARNING FOR INDUSTRIAL IOT MARKET– Company Profiles – (Overview, Product Portfolio, Financials, Developments)
8.1. Google LLC (United States)
8.2. Microsoft Corporation (United States)
8.3. IBM Corporation (United States)
8.4. Enveil (United States)
8.5. DataFleets (United States)
8.6. NVIDIA Corporation (United States)
8.7. FedML (United States)
8.8. Secure AI Labs (United States)
8.9. Aptima, Inc. (United States)
8.10. Databloom AI (United States)
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Frequently Asked Questions
The Global Federated Learning for Industrial IoT Market was estimated at USD 130.67 Million in 2022 and is anticipated to be a value of USD 294.68 Million by 2030, growing at a CAGR of 10.7% during the forecast period 2023-2030
The Global Federated Learning for Industrial IoT Market Drivers are the increasing concerns about data privacy and security and the widespread adoption of industrial IoT (IIoT)
Based on the Verticals, the Global Federated Learning for Industrial IoT Market is segmented into Banking, Financial Services, and Insurance, Healthcare and Life Sciences, Retail and E-commerce, Manufacturing, Energy, and Utilities, Automotive and Transportation, IT and Telecommunication, and Others.
The Europe region held the largest share of the Global Federated Learning for Industrial IOT Market in 2022
Google LLC, IBM Corporation, and NVIDIA Corporation are the leading players in the Global Federated Learning for Industrial IoT Market
