Automating aggregation strategy selection in federated learning
This work addresses the need for accessible and adaptive federated learning by automating a critical design decision, though it appears incremental as it builds on existing aggregation methods with automation.
The paper tackles the problem of selecting aggregation strategies in federated learning, which is challenging due to performance variations across datasets and conditions, and presents an automated framework that enhances robustness and generalization under non-IID conditions while reducing manual intervention.
Federated Learning enables collaborative model training without centralising data, but its effectiveness varies with the selection of the aggregation strategy. This choice is non-trivial, as performance varies widely across datasets, heterogeneity levels, and compute constraints. We present an end-to-end framework that automates, streamlines, and adapts aggregation strategy selection for federated learning. The framework operates in two modes: a single-trial mode, where large language models infer suitable strategies from user-provided or automatically detected data characteristics, and a multi-trial mode, where a lightweight genetic search efficiently explores alternatives under constrained budgets. Extensive experiments across diverse datasets show that our approach enhances robustness and generalisation under non-IID conditions while reducing the need for manual intervention. Overall, this work advances towards accessible and adaptive federated learning by automating one of its most critical design decisions, the choice of an aggregation strategy.