Trustworthy Federated Learning: Privacy, Security, and Beyond

• URL: http://arxiv.org/abs/2411.01583v1
• Date: Sun, 03 Nov 2024 14:18:01 GMT
• Title: Trustworthy Federated Learning: Privacy, Security, and Beyond
• Authors: Chunlu Chen, Ji Liu, Haowen Tan, Xingjian Li, Kevin I-Kai Wang, Peng Li, Kouichi Sakurai, Dejing Dou,
• Abstract summary: Federated Learning (FL) addresses concerns by facilitating collaborative model training across distributed data sources without transferring raw data. We conduct an extensive survey of the security and privacy issues prevalent in FL, underscoring the vulnerability of communication links and the potential for cyber threats. We identify the intricate security challenges that arise within the FL frameworks, aiming to contribute to the development of secure and efficient FL systems.
• Score: 37.495790989584584
• License:
• Abstract: While recent years have witnessed the advancement in big data and Artificial Intelligence (AI), it is of much importance to safeguard data privacy and security. As an innovative approach, Federated Learning (FL) addresses these concerns by facilitating collaborative model training across distributed data sources without transferring raw data. However, the challenges of robust security and privacy across decentralized networks catch significant attention in dealing with the distributed data in FL. In this paper, we conduct an extensive survey of the security and privacy issues prevalent in FL, underscoring the vulnerability of communication links and the potential for cyber threats. We delve into various defensive strategies to mitigate these risks, explore the applications of FL across different sectors, and propose research directions. We identify the intricate security challenges that arise within the FL frameworks, aiming to contribute to the development of secure and efficient FL systems.

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