Int'l : +1-503-894-6022 | Toll Free : +1-800-792-5285 | help@alliedmarketresearch.com
A14591 | Pages: 350 | Charts: 65 | Tables: 155 |
The global federated learning solutions market size was valued at $125.9 million in 2023, and is projected to reach $301.9 million by 2032, growing at a CAGR of 10.2% from 2024 to 2032. Federated learning is a machine learning method that has an algorithm across several decentralized edge devices or servers carrying local data samples. This method is in contrast with the traditional centralized machine learning techniques where all the local datasets are stored on a single server. In addition, this technique makes sure that the local data samples are identically dispersed in the server.
Federated learning is used to construct models on consumer behavior from the information pool of smartphones without disclosing personal data such as for next-word prediction, face detection, and voice recognition. Federated learning allows multiple vendors to build a common, machine learning model without sharing data, thus allowing to address critical issues such as data privacy & security, data access rights, and the ability to access diverse data.
The federated learning solutions industry study covers 20 countries. The research includes a segment analysis of each country in terms of value ($Million) for the projected period 2024-2032.
More than 1,500 product literature, industry releases, annual reports, and other such documents of major federated learning solutions industry participants along with authentic industry journals, trade associations' releases, and government websites have been reviewed for generating high-value industry insights.
The study integrated high-quality data, professional opinions and analysis, and critical independent perspectives. The research approach is intended to provide a balanced view of global markets and assist stakeholders in making educated decisions to achieve their most ambitious growth objectives.
Key Market Segments
Key Market Players