Entropy-based drift-aware federated learning solution robust against environments with heterogeneous data

Abstract

Entropy based federated learning is disclosed. In federated learning, a model is trained at multiple clients using corresponding local data. An entropy associated with the local training is determined and provided, along with a model update, to a central server. The central server selects specific clients to participate in the current aggregation operation based on the entropy values. This minimizes the number of drifted and noisy clients that are included in the aggregation operation. The model updates of the selected clients are aggregated and a new or updated global model is generated. To aid in accounting for data heterogeneity, the clients may be grouped and a new or updated global model may be generated for each of the groups using model updates from corresponding selected clients.

Dependencies are inferred from the claim text.