Global Federated Learning Market Analysis Report 2022-2028: Focus on Healthcare & Life Sciences, BFSI, Manufacturing, Automotive & Transportation, Energy & Utilities – ResearchAndMarkets. com

DUBLIN–(BUSINESS WIRE)–The “Global Federated Learning Market by Application (Drug Discovery, Industrial IoT, Risk Management), Vertical (Healthcare & Life Sciences, BFSI, Manufacturing, Automotive & Transportation, Energy & Utilities) and Region – Forecast up to ‘in 2028’ report has been added to from offer.

The global Federated Learning market size will grow from USD 127 Million in 2023 to USD 210 Million by 2028, at a compound annual growth rate (CAGR) of 10.6%

Key drivers including the ability to help businesses collaborate on a common machine learning (ML) prototype retaining machine insights and the power to control predictive features on connected devices without impacting user experience nor disclose private information, should drive the growth of federated learning solutions.

According to the AS-IS scenario, among the verticals, the automotive and transportation segment is expected to grow at the highest CAGR during the forecast period

The federated learning solutions market is segmented into verticals into BFSI, healthcare and life sciences, retail and e-commerce, energy and utilities, and manufacturing, automotive and transportation, IT and telecommunications and other sectors verticals (government, media and entertainment).

According to the 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, emphasis has been placed on data, end-to-end computer technology management and ML algorithm improvement, in addition to making automated vehicles reliable and secure for a seamless integration from one region of the world to another, even scanning personal information and privacy wirelessly.

Efficient learning chooses the most relevant data items to classify and add to the instruction pool. Additionally, they can use federated learning to retrain the network across many devices in a decentralized way using the specific information we’ll receive from each car to identify those imperfections and help prevent the car from hitting other bugs. -chicken.

According to the AS-IS scenario, among the regions, Asia-Pacific (APAC) is expected to grow at the highest CAGR during the forecast period

According to the AS-IS scenario, the federated learning market in APAC is expected to grow at the highest CAGR from 2023 to 2028. APAC is witnessing advanced and dynamic adoption of new technologies. Key countries such as India, Japan, Singapore, and China are focusing on implementing privacy and data security regulations in the coming years.

This would create an opportunity to implement federated learning solutions for data security and privacy. Many Asian countries are leveraging information-intensive big data and AI technologies to collect data from various data sources. The commercialization of big data, AI and IoT technologies and the need for further advancements to get the most out of these technologies are expected to increase their adoption in the future.

Key Players of Federated Learning Market are NVIDIA (US), Cloudera (US), IBM (US), Microsoft (US), Google (US), Intel (US) United States), Owkin (United States), Intellegens (United Kingdom), Edge Delta (United States), Enveil (United States), Lifebit (United Kingdom), DataFleets (United States), Secure AI Labs ( United States) and Sherpa.AI (Spain).

Market dynamics


  • Growing need to increase learning across devices and organizations

  • Ability to provide better data privacy and security by training algorithms on decentralized devices

  • Growing adoption of federated learning in various applications for data privacy

  • Ability of federated learning to respond to the difficulty of preserving the anonymity of individuals


  • Lack of qualified technical expertise


  • Federated learning allows distributed participants to collaboratively learn a shared model while maintaining data locally

  • Ability to enable predictive features on smart devices without affecting user experience and without disclosing private information


  • High latency and communication inefficiency issues

  • System integration and interoperability issue

  • Indirect information leak

Use case analysis

  • WeBank and car rental service provider enable insurance industry to reduce data traffic breaches with federated learning

  • Federated learning enables healthcare companies to encrypt and protect patient data

  • WeBank and Extreme Vision Launch Online Visual Object Detection Platform Powered by Federated Learning to Store Data in the Cloud

  • WeBank introduced a federated learning model for anti-money laundering

  • Adopting Intellegens Solution Can Help Clinics Analyze Heart Rate Data

Technological analysis

  • Federated Learning vs Distributed Machine Learning

  • Federated Learning vs Edge Computing

  • Federated Learning vs Federated Database Systems

  • Federated learning vs swarm learning

Research Projects: Federated Learning

  • Machine Learning Registry Orchestration for Drug Discovery (MELLODDY)

  • Speakers

  • FedAI

  • PaddlePaddle

  • FeatureCloud

  • Musketeer Project

Regulatory landscape

  • Regulatory bodies, government agencies and other organizations

  • Regulatory implications and industry standards

  • General Data Protection Regulation

  • SEC Rule 17A-4

  • ISO/IEC 27001

  • System and organization checks 2 Type II conformity

  • Financial Sector Regulatory Authority

  • Freedom of Information Act

  • Health Insurance Portability and Accountability Act

Company Profiles

Key players

  • Nvidia

  • Google

  • Microsoft

  • IBM

  • Cloudera

  • Intel

  • owkin

  • Intellegenes

  • On-board delta

  • Mail

  • piece of life

  • Data fleets

Other key players

  • Secure AI labs

  • Sherpa.AI

  • Decentralized machine learning

  • Conscious

  • Apheris

  • Precision

  • FedML

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