The global demand for Federated Learning Market is presumed to reach the market size of nearly USD 457.39 BN by 2028 from USD 210.82 BN in 2021 with a CAGR of 11.7% under the study period 2022 - 2028.
Federated learning is a machine learning that uses several distributed network edges or servers that each keep data samples without sharing them to build an algorithm. Federated learning enables several players to develop an identical, reliable machine-learning model without sharing data, settling crucial concerns such as privacy, security, access rights, and diverse data availability. In federated learning, data is gathered from various devices, including laptops, tablets, and smartphones, and is combined into a centralized server. The algorithms then read the data by themselves and produce it.
Market Dynamics
The rising requirement to enhance learning between devices and organizations is the primary driver of the market need for global federated learning. Furthermore, the federated learning sector is projected to grow due to the growing demand to permit analytical approaches without compromising personal information. The popularity of federated learning has increased due to growing concerns about data privacy and security. More autonomous car development is anticipated in the upcoming years, which will enhance federated learning's prevalence. Companies may improve their product offers by developing cutting-edge, innovative solutions. Federated learning is also gaining a market presence in the industrial sector.
The research report covers Porter’s Five Forces Model, Market Attractiveness Analysis, and Value Chain analysis. These tools help to get a clear picture of the industry’s structure and evaluate the competition attractiveness at a global level. Additionally, these tools also give an inclusive assessment of each segment in the global market of federated learning. The growth and trends of federated learning industry provide a holistic approach to this study.
Market Segmentation
This section of the federated learning market report provides detailed data on the segments at country and regional level, thereby assisting the strategist in identifying the target demographics for the respective product or services with the upcoming opportunities.
By Application
- Industrial Internet Of Things
- Drug Discovery
- Risk Management
- Augmented And Virtual Reality
- Data Privacy Management
- Others
By Industry Vertical
- Retail
- Automotive
- IT & Telecommunication
- Healthcare
- BFSI
- Manufacturing
- Others
Regional Analysis
This section covers the regional outlook, which accentuates current and future demand for the Federated Learning market across North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa. Further, the report focuses on demand, estimation, and forecast for individual application segments across all the prominent regions.
Global Federated Learning Market Share by Region (Representative Graph)
The research report also covers the comprehensive profiles of the key players in the market and an in-depth view of the competitive landscape worldwide. The major players in the federated learning market include Apheris AI GmbH, Acuratio, Consilient, Cloudera Inc., DataFleets, Decentralized Machine Learning, Edge Delta, Enveil, FedML, Google Inc., IBM Corporation, Intel Corporation, Lifebit, NVIDIA Corporation, Secure AI Labs, and Sherpa.AI. This section consists of a holistic view of the competitive landscape that includes various strategic developments such as key mergers & acquisitions, future capacities, partnerships, financial overviews, collaborations, new product developments, new product launches, and other developments.
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