Federated Learning Overview: Frameworks, Challenges, and Future Directions

Abstract

Federated Learning (FL) is a highly regarded distributed machine learning framework that aims to enable collaborative modeling among multiple parties without disclosing raw data. Compared to centralized learning, FL allows each participant to train models independently on-site and upload model update parameters rather than data to a central server, thereby improving model performance while effectively protecting data privacy. As artificial intelligence is increasingly applied in fields such as healthcare, finance, and industrial IoT, data privacy and compliance requirements are becoming increasingly stringent, highlighting the significant application potential and research value of federated learning. This paper systematically reviews the basic theories, core algorithms, and technical approaches of federated learning, focusing on research progress in areas such as communication efficiency, data heterogeneity, privacy protection, and trust mechanisms. The study also explored the development prospects and future directions of federated learning in the medical, financial, and precision computing fields. At the same time, we conducted a more in-depth analysis of the main challenges currently faced and analyzed potential future research directions in order to provide a reference for the continued development of this field.