Fast-Convergent Federated Learning with Adaptive Weighting

Understand

Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a global model under the orchestration of a central server while keeping privacy-sensitive data locally.

• The non-independent-and-identically-distributed (non-IID) data samples across participating nodes slow model training and impose additional communication rounds for FL to converge.
• In this paper, we propose Federated Adaptive Weighting (FedAdp) algorithm that aims to accelerate model convergence under the presence of nodes with non-IID dataset.
• We observe the implicit connection between the node contribution to the global model aggregation and data distribution on the local node through theoretical and empirical analysis.