In federated learning, particularly in cross-device scenarios, secure aggregation has recently gained popularity as it effectively defends against inference attacks by malicious aggregators. However, secure aggregation often requires additional communication overhead and can impede the convergence rate of the global model, which is particularly challenging in wireless network environments with extremely limited bandwidth. Therefore, achieving efficient communication compression under the premise of secure aggregation presents a highly challenging and valuable problem. In this work, we propose a novel uplink communication compression method for federated learning, named FedMPQ, which is based on multi shared codebook product quantization.Specifically, we utilize updates from the previous round to generate sufficiently robust codebooks. Secure aggregation is then achieved through trusted execution environments (TEE) or a trusted third party (TTP).In contrast to previous works, our approach exhibits greater robustness in scenarios where data is not independently and identically distributed (non-IID) and there is a lack of sufficient public data. The experiments conducted on the LEAF dataset demonstrate that our proposed method achieves 99% of the baseline's final accuracy, while reducing uplink communications by 90-95%

在本研究中，我们提出了一种名为FedMPQ的新型上行通信压缩方法，它基于多共享码本乘积量化。通过利用前一轮的更新来生成足够强健的码本，我们实现了安全聚合。与以往的方法相比，我们的方法在数据不独立且分布不一致（non-IID）且缺乏足够的公共数据的情况下表现出更高的鲁棒性。对LEAF数据集进行的实验证明，我们提出的方法在减少上行通信量的同时，实现了基准模型的99%最终准确度。

FedMPQ：具有多编码本产品量化的安全和通信高效的联邦学习