Privacy-Preserving Aggregation in Federated Learning: A Survey

Abstract

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms and growing concerns over personal data privacy, Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention from both academia and industry. Practical PPFL typically allows multiple participants to individually train their machine learning models, which are then aggregated to construct a global model in a privacy-preserving manner. As such, Privacy-Preserving Aggregation (PPAgg) as the key protocol in PPFL has received substantial research interest. This survey aims to fill the gap between a large number of studies on PPFL, where PPAgg is adopted to provide a privacy guarantee, and the lack of a comprehensive survey on the PPAgg protocols applied in FL systems. In this survey, we review the PPAgg protocols proposed to address privacy and security issues in FL systems. The focus is placed on the construction of PPAgg protocols with an extensive analysis of the advantages and disadvantages of these selected PPAgg protocols and solutions. Additionally, we discuss the open-source FL frameworks that support PPAgg. Finally, we highlight important challenges and future research directions for applying PPAgg to FL systems and the combination of PPAgg with other technologies for further security improvement.

A Survey on Federated Learning PoisoningAttacks and Defenses

“…(10) Ziyao Liu et al [29] summarized federated learning privacy attacks and defense schemes, and they also presented the current challenges related to this area.…”

“…Major contents [20] early work that concludes data poisoning, model poisoning, and defense [21] problems of communication, poisoning attacks, inference attacks and privacy leakage [22] the concept of semi-supervised federated learning and applications [23] defenses against model poisoning and privacy inference attacks [24] block chain-based privacy protection for federated learning [25] privacy protection classification of federated learning and the defenses [26] federated learning privacy protection, communication overhead, and malicious participant defenses [27] defense methods for model poisoning [28] federated learning privacy protection convergence programme [29] survey and evaluation of federated learning privacy attacks and defenses programs [30] federated learning robustness, privacy attacks, and defenses…”