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.

这项研究回顾了在联邦学习系统中应用的 PPAgg 协议，并对构建 PPAgg 协议的优缺点进行了广泛分析，同时讨论了支持 PPAgg 的开源 FL 框架，强调了将 PPAgg 应用于 FL 系统并将其与其他技术相结合以进一步提高安全性的重要挑战和未来研究方向。

联邦学习中的隐私保护聚合：一项调研

Privacy-Preserving Aggregation in Federated Learning: A Survey

In this work, we present a federated version of the state-of-the-art Neural
Collaborative Filtering (NCF) approach for item recommendations. The system,
named FedNCF, enables learning without requiring users to disclose or transmit
their raw data. Data localization preserves data privacy and complies with
regulations such as the GDPR. Although federated learning enables model
training without local data dissemination, the transmission of raw clients'
updates raises additional privacy issues. To address this challenge, we
incorporate a privacy-preserving aggregation method that satisfies the security
requirements against an honest but curious entity. We argue theoretically and
experimentally that existing aggregation algorithms are inconsistent with
latent factor model updates. We propose an enhancement by decomposing the
aggregation step into matrix factorization and neural network-based averaging.
Experimental validation shows that FedNCF achieves comparable recommendation
quality to the original NCF system, while our proposed aggregation leads to
faster convergence compared to existing methods. We investigate the
effectiveness of the federated recommender system and evaluate the
privacy-preserving mechanism in terms of computational cost.

本文提出一种改进的基于联邦学习的神经协同过滤推荐系统，名为 FedNCF，通过隐私保护聚合提高用户数据的安全性和数据保护合规性，并且在与原始 NCF 系统相比不降低推荐质量的情况下，实现更快的模型收敛速度。