A Comprehensive Investigation of Federated Learning Frameworks: Architectures, Security Challenges and Future Directions

Abstract

With the rapid development of Artificial Intelligence (AI) and Machine Learning (ML) technologies, federated learning models have been widely applied in various fields such as financial risk management, healthcare, and intelligent recognition. This study primarily analyzes the architectural characteristics, workflow, advantages, and disadvantages of three classical federated learning frameworks, namely horizontal federated learning, vertical federated learning, and federated transfer learning to identify the most suitable application scenarios, potential risks, and corresponding security solutions for each model. Furthermore, the paper discusses several major challenges commonly faced by different federated learning models, including issues such as regulatory compliance and cross-domain interoperability. Based on the analytical findings, it also explores potential future development directions of federated learning, such as the maturation of a trustworthy federated AI ecosystem and governance. Finally, the study concludes that implementing multi-layer privacy protection strategies, encrypted gradient exchange mechanisms and other methods can effectively reduce security risks in federated learning systems.