FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity

arXiv:2404.09816v1 Announce Type: new
Abstract: The interest in federated learning has surged in recent research due to its unique ability to train a global model using privacy-secured information held locally on each client. This paper pays particular attention to the issue of client-side model heterogeneity, a pervasive challenge in the practical implementation of FL that escalates its complexity. Assuming a scenario where each client possesses varied memory storage, processing capabilities and network bandwidth - a phenomenon referred to as system …

abstract arxiv attention challenge client cs.cr cs.lg federated learning global information issue network paper personalized privacy pruning research train type