3 accepted papers
Split Federated Learning (SFL) has emerged as a pivotal paradigm for privacy-preserving distributed training on resource-constrained edge devices by partitioning neural networks between clients and a server. A critical design choice in SFL is the split layer, which determines the computation distrib
Personalized federated graph neural networks (PFGNN) are an emerging technology that allows multiple graph data owners to collaboratively train personalized models without sharing raw data. However, the Non-IID nature of graph data can cause the coupling of global and local knowledge parameters, whi
Carefully selecting clients to participate in aggregation can assist the global model in achieving better performance. However, existing research on federated heterogeneous graph learning (FHGL) has shown limited attention to the client selection (CS) problem. Current CS algorithms face challenges i…