Strategic partnerships to accelerate technological development to meet substantial market demand

DUBLIN, 23 Aug 2021 / PRNewswire / – The report “Federated Learning: A New Approach to Building AI Models” has been added to offer.

Federated learning is a distributed ML architecture that allows training a global model using decentralized data. It is intended to use data from an entire organization accurately and efficiently.

To help companies gain valuable insights into this emerging technique, this report offers insight into the Federated Learning industry, market dynamics, major market players, research directions, areas of research. key application and recent developments.

Traditional machine learning (ML) models are centralized and involve large amounts of data. However, the urgency of ensuring data confidentiality and complying with strict regulations imposed across regions have contributed to the emergence of a powerful new alternative technique, federated learning. Instead of acquiring data from a central server or cloud, federated learning enables localized model training.

The technique guarantees the preservation of confidentiality and better global models are formed without the exchange of raw data containing private and sensitive information. Attracted by this powerful privacy technique, a growing number of market players, academics and end-use industries are embracing federated learning at an unprecedented rate.

The following chapters are included:

  • Introducing federated learning
  • Market Forecasts, Drivers and Challenges
  • Main lines of research for federated learning
  • IP landscape analysis
  • Key factors and recent technological developments
  • Companies in action, including Edgify, Owkin,, Sherpa Europe and WeBank
  • Growth opportunities

Main topics covered:

1.0 Strategic imperatives
1.1 Why is it more and more difficult to develop? The strategic imperative: the factors of pressure on growth
1.2 The strategic imperative
1.3 The impact of the three main 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

2.0 Analysis of growth opportunities
2.1 Scope of the research
2.2 Research methodology
2.3 Research methodology explained
2.4 Main findings

3.0 Overview of Federated Learning
3.1 Federated learning emerges as a new concept for ML implementation
3.2 Different types of federated learning
3.3 Typical federated learning applications

4.0 Market Forecast, Key Drivers and Challenges
4.1 Federated Learning Market Forecast and Regional Outlook
4.2 Growth drivers and constraints of the federated learning market

5.0 Key Research Directions for Federated Learning
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 confidentiality, security and management of resources

6.0 IP Landscape Analysis
6.1 China and the United States has the most patent publications
6.2 WeBank and IBM lead patenting activities around the world

7.0 Key Factors and Recent Technological Developments
7.1 Key factors in the development of federated learning in the market
7.2 Tech giants drive industry innovation
7.3 Market, intelligent IT and medical research as new areas of interest for federated learning

8.0 Businesses in action
8.1 Edgify Ltd., United Kingdom
8.2 Owkin Inc., United States
8.3 Limited, United Kingdom
8.4 Sherpa Europe SL, Spain
8.5 WeBank Co., Ltd., China

9.0 Universe of growth opportunities
9.1 Growth opportunity 1: Open source strategy for building a federated learning ecosystem
9.2 Growth opportunity 2: Strategic partnerships to accelerate technological development to meet substantial market demand
9.3 Growth opportunity 3: Federated learning to comply with privacy regulations

10.0 Key contacts

For more information on this report, visit

Media contact:

Research and markets
Laura Wood, senior
[email protected]

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SOURCE Research and Markets

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