A Survey of Security Strategies in Federated Learning: Defending Models, Data, and Privacy

Abstract

Federated Learning (FL) has emerged as a transformative paradigm in machine learning, enabling decentralized model training across multiple devices while preserving data privacy. However, the decentralized nature of FL introduces significant security challenges, making it vulnerable to various attacks targeting models, data, and privacy. This survey provides a comprehensive overview of the defense strategies against these attacks, categorizing them into data and model defenses and privacy attacks. We explore pre-aggregation, in-aggregation, and post-aggregation defenses, highlighting their methodologies and effectiveness. Additionally, the survey delves into advanced techniques such as homomorphic encryption and differential privacy to safeguard sensitive information. The integration of blockchain technology for enhancing security in FL environments is also discussed, along with incentive mechanisms to promote active participation among clients. Through this detailed examination, the survey aims to inform and guide future research in developing robust defense frameworks for FL systems.