Research of the Technical Principles, Practical Applications, Challenges and Future Development Trends of Federated Learning

DOI

10.2991/978-94-6463-823-3_111How to use a DOI?

Keywords

Federated Learning; Privacy Computing; Distributed Optimization; Heterogeneous Collaboration; Edge Intelligence

Abstract

This paper provides a comprehensive and in-depth review of federated learning technology, systematically elaborating on its theoretical foundation, technical implementation, application scenarios, and development trajectory. Firstly, it constructs a theoretical framework for federated learning from three dimensions: distributed optimization, privacy protection, and machine learning, and analyzes in detail its core algorithms and system architecture. Subsequently, it delves into the practical implementation and industry solutions of federated learning in typical application fields such as healthcare, smart finance, intelligent manufacturing, and smart cities. This paper innovatively proposes a five-dimensional evaluation system for federated learning technology (privacy, efficiency, accuracy, fairness, and scalability), and based on this, analyzes the key technological breakthroughs in current research. Finally, it offers a forward-looking perspective on the future development directions of federated learning from five aspects: algorithm innovation, security enhancement, heterogeneous collaboration, incentive mechanisms, and standardization construction, providing a systematic reference framework for related research.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Huanyuan Li
PY  - 2025
DA  - 2025/08/31
TI  - Research of the Technical Principles, Practical Applications, Challenges and Future Development Trends of Federated Learning
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 1157
EP  - 1165
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_111
DO  - 10.2991/978-94-6463-823-3_111
ID  - Li2025
ER  -