Architectural Blueprint for Optimizing Federated Learning in Edge Computing Environments
The author proposes a three-tier architecture to enhance federated learning efficiency in edge computing by addressing data heterogeneity and computational constraints. The approach integrates clients, edge layers, and fedge layer to manage diverse models effectively.