IEEE

Leveraging Game Theory and XAI for Data Quality-Driven Sample and Client Selection in Trustworthy Split Federated Learning

Abstract: In Federated Learning (FL) systems, clients share updates derived from their local data with the server while maintaining privacy. The server aggregates these updates to refine the global ...
IEEE

Split Averaging: Bridging the Heterogeneity Gap in Clients Data for Federated Learning

Abstract: Federated Learning (FL) has gained significant prominence to overcome the issue of data silos in various domains. However, since its introduction FL has been confronted with the presence of ...