The Technological Progress, Applications, and Challenges of Federated Learning

With the advent of the era of big data, the exponential growth of data generation has provided unprecedented opportunities for innovation and insight in various fields. However, increasing privacy and security concerns and the existence of the phenomenon of “data silos” limit the collaborative utilization of data. This paper systematically discusses the technological progress of federated learning, including its basic framework, model optimization, communication efficiency improvement, privacy protection mechanism, and integration with other technologies. It then analyzes the broad applications of federated learning in healthcare, the Internet of Things, Internet of Vehicles, smart cities, and financial services, and summarizes its challenges in data heterogeneity, communication overhead, privacy protection, scalability, and security. Finally, this paper looks forward to the future development direction of federated learning and proposes potential research paths in efficient algorithm design, privacy protection mechanism optimization, heterogeneous data processing, and cross-industry collaboration.

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