Global Federated Learning Market Report 2022 to 2028: Players Include IBM, Microsoft, Intel, Google and Cloudera - ResearchAndMarkets.com

The "Global Federated Learning Market Size, Share & Industry Trends Analysis Report By Application, By Vertical, By Regional Outlook and Forecast, 2022 - 2028" report has been added to ResearchAndMarkets.com's offering.

The Global Federated Learning Market size is expected to reach $198.7 Million by 2028, rising at a market growth of 11.1% CAGR during the forecast period.

Federated learning can be described as a machine learning approach that distributes an algorithm among a number of decentralized end devices or servers that each have local data samples. This strategy differs from standard centralized machine learning methods, which store all local datasets on a single server. Additionally, this technique ensures that the local data samples are disseminated to the server in the same way.

Federated learning can be utilized to build consumer behavior models from the data pool of smartphones without revealing personal information, like for next-word prediction, voice recognition, facial identification, and other applications. Federated learning enables various vendors to develop a shared machine learning algorithm without sharing data, allowing crucial issues like data access rights, data privacy and security, and the capacity to access heterogeneous data to be addressed. Defense, telecommunications, and medicines are among the businesses that can leverage federated learning to optimize their operations.

The growing need for improved data protection and privacy, as well as the increasing requirement to adapt data in real-time to optimize conversions automatically are driving the advancement of the federated learning solutions market. Moreover, by retaining data on devices, these solutions assist organizations in leveraging machine learning models, boosting the federated learning market forward.

Furthermore, the ability to provide predictive features on the latest smart devices without compromising the consumer experience or divulging private information is providing lucrative opportunities for the federated learning market to develop throughout the coming years.

Market Growth Factors

Enhanced data privacy in numerous applications

Due to federated learning, the manner in which ML approaches are offered is evolving. Companies are increasing their efforts on performing a thorough investigation of federated learning. Using federated learning, companies may reinforce their existing algorithms and improve their AI applications.

The demand for improved learning is increasing among both gadgets and companies. In the healthcare field, federated learning could help healthcare personnel deliver high-quality outcomes while also accelerating drug development. For example, FADNet, a new peer-to-peer technique, is a remedy for centralized learning inadequacies.

Enables collaborative learning among various users

Federated learning, rather than keeping data on a single computer or data mart, stores data on original sources, like smartphones, manufacturing detection equipment, other end devices, and machine learning machines are trained on the go. This aids in decision-making before being sent back to a centralized computer. For example, federated learning is widely used in the finance sector for debt risk assessments.

Typically, banks use whitelisting processes to keep customers out of the Federal Reserve System based on their credit card information. Risk assessment variables, like taxation and reputation, may be employed by working with other financial institutions and eCommerce businesses.

Market Restraining Factors

Scarcity of skilled technical professionals

Many businesses encounter a significant impediment when integrating machine learning into existing workflows due to a scarcity of trained people, particularly IT specialists. Because federated learning systems are a new concept, it is difficult for personnel to grasp and execute them.

Recruiting and maintaining technical skills became a major concern for several firms due to a scarcity of skilled candidates to incorporate federated learning projects that include difficult methodologies, such as machine learning. As an organization, they must develop a growing range of talents and job titles. Organizations, for example, require experts that can administer and comprehend the current federated learning architecture connected with the installation and maintenance of machine learning algorithms.

Scope of the Study

Market Segments Covered in the Report:

By Application

  • Drug Discovery
  • Risk Management
  • Online Visual Object Detection
  • Data Privacy & Security Management
  • Industrial Internet of Things
  • Augmented Reality/Virtual Reality
  • Shopping Experience Personalization
  • Others

By Vertical

  • Healthcare & Life Sciences
  • BFSI
  • IT & Telecommunication
  • Energy & Utilities
  • Manufacturing
  • Automotive & Transportation
  • Retail & Ecommerce
  • Others

By Geography

  • North America
  • Europe
  • Asia Pacific
  • LAMEA
  • Brazil

Key Market Players

  • IBM Corporation
  • Microsoft Corporation
  • Intel Corporation
  • Google LLC
  • Cloudera, Inc.
  • NVIDIA Corporation
  • Edge Delta, Inc.
  • DataFleets Ltd. (LiveRamp Holdings, Inc.)
  • Enveil
  • Secure AI Labs, Inc.

For more information about this report visit https://www.researchandmarkets.com/r/jau4w7

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