Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth

arXiv:2405.03248v1 Announce Type: new
Abstract: Federated learning can train models without directly providing local data to the server. However, the frequent updating of the local model brings the problem of large communication overhead. Recently, scholars have achieved the communication efficiency of federated learning mainly by model compression. But they ignore two problems: 1) network state of each client changes dynamically; 2) network state among clients is not the same. The clients with poor bandwidth update local model slowly, which leads …

abstract arxiv bandwidth communication compression cs.ai cs.lg data dynamic efficiency federated learning however scholars server train type