Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering

FedClust proposes a novel approach to Clustered Federated Learning (CFL) by leveraging correlations between local model weights and client data distributions, outperforming baseline methods in accuracy and communication costs.