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Federated Learning: New Approach to Building AI Models
Federated Learning: New Approach to Building AI Models
Leveraging Open Source Tools to Accelerate Technology Development across Organizations and Regions
RELEASE DATE
29-Jul-2021
29-Jul-2021
REGION
Global
Global
Research Code: DA0B-01-00-00-00
SKU: IT04364-GL-TR_25637
$4,950.00
In stock
SKU
IT04364-GL-TR_25637
Description
Traditional machine learning (ML) models are centralized and involve vast amounts of data. However, both the urgency to guarantee data privacy and to abide by strict regulations imposed across regions have contributed to the emergence of a new and powerful alternative technique, federated learning. Instead of acquiring data from a central server or cloud, federated learning allows localized model training. The technique ensures privacy preservation, and better global models are trained without exchanging raw data that holds private and sensitive information. Attracted to this powerful privacy-protecting technique, a growing number of market participants, academics, and end-use industries are adopting federated learning at an unprecedented rate.
Federated learning is a distributed ML architecture that enables a global model to be trained using decentralized data. It is intended to utilize data from across an organization accurately and effectively. To help companies gain valuable insights about this emerging technique, this report offers an overview of the federated learning industry, market dynamics, key market players, research directions, key application areas, and recent developments.
The following chapters are included:
- Overview of federated learning
- Market forecast, drivers, and challenges
- Key research directions for federated learning
- IP landscape analysis
- Key enablers and recent technology developments
- Companies to action, including Edgify, Owkin, Fetch.ai, Sherpa Europe, and WeBank
- Growth opportunities
RESEARCH: INFOGRAPHIC
This infographic presents a brief overview of the research, and highlights the key topics discussed in it.Click image to view it in full size
Table of Contents
1.1 Why Is It Increasingly Difficult to Grow?The Strategic Imperative 8™: Factors Creating Pressure on Growth
1.2 The Strategic Imperative 8™
1.3 The Impact of the Top Three Strategic Imperatives on Building AI Models for the Federated Learning Industry
1.4 About the Growth Pipeline Engine™
1.5 Growth Opportunities Fuel the Growth Pipeline Engine™
2.1 Research Scope
2.2 Research Methodology
2.3 Research Methodology Explained
2.4 Key Findings
3.1 Federated Learning Emerging as New Design for ML Implementation
3.2 Different Federated Learning Types
3.3 Typical Federated Learning Applications
4.1 Federated Learning Market Forecast and Regional Insights
4.2 Growth Drivers and Restraints of Federated Learning Market
5.1 Research Directions for Federated Learning
5.2 Various Research Topics Related to System Model Design and Application Areas
5.3 Methods to Ensure Privacy, Security, and Resource Management
6.1 China and the US Demonstrate the Most Patent Publications
6.2 WeBank and IBM Lead Patenting Activities across the Globe
7.1 Key Enablers of Developing Federated Learning in the Market
7.2 Technology Giants Driving Industry Innovation
7.3 Marketplace, Intelligent Computing Service, and Medical Research as Emerging Focus Areas for Federated Learning
8.1 Edgify Ltd., UK
8.2 Owkin Inc., US
8.3 Fetch.ai Limited, UK
8.4 Sherpa Europe S.L., Spain
8.5 WeBank Co., Ltd., China
9.1 Growth Opportunity 1: Open-source Strategy for Building Federated Learning Ecosystem
9.1 Growth Opportunity 1: Open-source Strategy for Building Federated Learning Ecosystem (continued)
9.2 Growth Opportunity 2: Strategic Partnerships for Accelerating Technology Development to Meet Substantial Market Demand
9.2 Growth Opportunity 2: Strategic Partnerships for Accelerating Technology Development to Meet Substantial Market Demands (continued)
9.3 Growth Opportunity 3: Federated Learning for Complying with Privacy-Preserving Regulations
9.3 Growth Opportunity 3: Federated Learning for Complying with the Privacy-Preserving Regulations (continued)
10.1 Key Contacts
11.1 Your Next Steps
11.2 Why Frost, Why Now?
Legal Disclaimer
Traditional machine learning (ML) models are centralized and involve vast amounts of data. However, both the urgency to guarantee data privacy and to abide by strict regulations imposed across regions have contributed to the emergence of a new and powerful alternative technique, federated learning. Instead of acquiring data from a central server or cloud, federated learning allows localized model training. The technique ensures privacy preservation, and better global models are trained without exchanging raw data that holds private and sensitive information. Attracted to this powerful privacy-protecting technique, a growing number of market participants, academics, and end-use industries are adopting federated learning at an unprecedented rate.
Federated learning is a distributed ML architecture that enables a global model to be trained using decentralized data. It is intended to utilize data from across an organization accurately and effectively. To help companies gain valuable insights about this emerging technique, this report offers an overview of the federated learning industry, market dynamics, key market players, research directions, key application areas, and recent developments.
The following chapters are included:
- Overview of federated learning
- Market forecast, drivers, and challenges
- Key research directions for federated learning
- IP landscape analysis
- Key enablers and recent technology developments
- Companies to action, including Edgify, Owkin, Fetch.ai, Sherpa Europe, and WeBank
- Growth opportunities
| No Index | No |
|---|---|
| Podcast | No |
| Author | Weihao Hung |
| Industries | Information Technology |
| WIP Number | DA0B-01-00-00-00 |
| Is Prebook | No |