FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization

arXiv:2404.13575v1 Announce Type: new
Abstract: 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 …

aggregation arxiv attacks can communication convergence cs.ai cs.cr device effectively federated federated learning global malicious product rate