FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity

arXiv:2404.09259v1 Announce Type: new
Abstract: Federated learning (FL) facilitates a privacy-preserving neural network training paradigm through collaboration between edge clients and a central server. One significant challenge is that the distributed data is not independently and identically distributed (non-IID), typically including both intra-domain and inter-domain heterogeneity. However, recent research is limited to simply using averaged signals as a form of regularization and only focusing on one aspect of these non-IID challenges. Given these limitations, this paper clarifies these two non-IID …

abstract arxiv challenge collaboration contrast cs.ai cs.cv data distributed distributed data domain edge feature federated learning however network network training neural network paradigm privacy research server through training type