Federated Learning Market Size Worth $266.77 Million By 2030 | CAGR: 10.7%

The global federated learning market size is expected to reach USD 266.77 million by 2030, according to a new study by Polaris Market Research. The report “Federated Learning Market Share, Size, Trends, Industry Analysis Report, By Application (Industrial Internet of Things, Drug Discovery, Risk Management, Augmented and Virtual Reality, Data Privacy Management, Others); By Industry Vertical; By Region; Segment Forecast, 2022 - 2030” gives a detailed insight into current market dynamics and provides analysis on future market growth.

ML enables development of applications that learn and adapt from data with greater accuracy over time without explicit instructions. It includes algorithms that are capable to create systems by understanding input and output information that describes them. ML can be provided by several types of algorithms such as decision trees, neural networks, and inductive logic programming.

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Moreover, federated learning is the new era of secure artificial intelligence (AI), as this technique trains, tests, and provide data security. In addition, the data is difficult to be hacked due to the introduction of such method. Federated learning method has a huge potential, as it helps in securing sensitive and personal information of people and businesses. It also, aggregates outcome and recognizes familiar samples from various users, therefore, making the module robust.

Advent of cloud computing, deep federated learning, and parallel computing architectures; and rise in demand for AI capabilities such as image, speech, text, and recognition have fast-tracked the ongoing research associated with AI, fueling a new trend of rise in investment in developing premium AI hardware, which is capable of advancing application development. In addition, increase in interest of aerospace players to deploy AI across several applications is expected to boost demand for federated learning during the forecast period.

Moreover, the adoption of predictive and prescriptive analytics has been increasing. Predictive analytics is used to know the likelihood of certain events happening in the future, and prescriptive analytics provides solutions and actions to be taken in case of occurrence of such events. End-user industries have been recognizing the benefits of these analytical solutions and utilizing them to gain an edge over competitors. Widespread applications of predictive and prescriptive analytics would provide growth opportunities in the industry.

Federated Learning Market Report Highlights

• The market for industrial internet of things segment is expected to hold a significant share in 2030 as its adoption offers greater performance and efficiency across industries
• The automotive segment is expected to be the fastest growing segment during the forecast period. This is due rise in adoption for artificial intelligence (AI) enabled federated learning technique
• Europe region will lead the global market by 2030 due to rising initiatives undertaken by the governments towards healthcare industry
• The global market is highly competitive owing to the existence of large market players with global presence including Google Inc., IBM Corporation, Intel Corporation, Lifebit, NVIDIA Corporation, Secure AI Labs, and Sherpa.AI.

Polaris Market Research has segmented the federated learning market report based on application, industry vertical, and region.:

Federated Learning, Application Outlook (Revenue - USD Million, 2018 - 2030)

• Industrial Internet of Things
• Drug Discovery
• Risk Management
• Augmented and Virtual Reality
• Data Privacy Management
• Others                                    

Federated Learning, Industry Vertical Outlook (Revenue - USD Million, 2018 - 2030)

• Retail
• Automotive
• IT & Telecommunication
• Healthcare
• BFSI
• Manufacturing
• Others

Federated Learning, Regional Outlook (Revenue - USD Million, 2018 - 2030)

• North America
  • U.S
  • Canada
• Europe
  • Germany
  • UK
  • France
  • Italy
  • Spain
  • Russia
  • Netherlands
• Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • Indonesia
  • Malaysia
• Latin America
  • Argentina
  • Brazil
  • Mexico
• Middle East & Africa
  • UAE
  • Saudi Arabia
  • Israel
  • South Africa