Architectural Blueprint For Heterogeneity-Resilient Federated Learning
Satwat Bashir Tasos Dagiuklas Kasra Kassai Muddesar Iqbal
This paper proposes a novel three tier architecture for federated learning to
optimize edge computing environments. The proposed architecture addresses the
challenges associated with client data heterogeneity and computational
constraints. It introduces a scalable, privacy preserving framework that
enhances the efficiency of distributed machine learning. Through
experimentation, the paper demonstrates the architecture capability to manage
non IID data sets more effectively than traditional federated learning models.
Additionally, the paper highlights the potential of this innovative approach to
significantly improve model accuracy, reduce communication overhead, and
facilitate broader adoption of federated learning technologies.