Federated Learning (FL) has been proposed as a privacy-preserving solution for machine learning. However, recent works have shown that Federated Learning can leak private client data through membership attacks. In this paper, we show that the effectiveness of these attacks on the clients negatively correlates with the size of the client datasets and model complexity. Based on this finding, we propose model-agnostic Federated Learning as a privacy-enhancing solution because it enables the use of models of varying complexity in the clients. To this end, we present $\texttt{MaPP-FL}$, a novel privacy-aware FL approach that leverages model compression on the clients while keeping a full model on the server. We compare the performance of $\texttt{MaPP-FL}$ against state-of-the-art model-agnostic FL methods on the CIFAR-10, CIFAR-100, and FEMNIST vision datasets. Our experiments show the effectiveness of $\texttt{MaPP-FL}$ in preserving the clients' and the server's privacy while achieving competitive classification accuracies.

基于研究成果，我们提出了一种新的隐私感知性联邦学习方法$	exttt{MaPP-FL}$，通过在客户端上利用模型压缩并在服务器上保持完整模型，实现了同时保护客户端和服务器隐私的功能，并取得了有竞争力的分类准确性。

利用模型压缩解决联合学习中的会员推导攻击