Automating aggregation strategy selection in federated learning

2026-04-09

AI Summary

This work addresses the critical challenge of selecting effective aggregation strategies in federated learning, which significantly impacts model performance yet depends intricately on data heterogeneity, device diversity, and resource constraints, with no existing automated solution. The paper proposes the first end-to-end adaptive framework that seamlessly integrates large language model–based reasoning with lightweight genetic search to automatically identify optimal aggregation strategies without human intervention. Evaluated across diverse non-IID settings and datasets, the approach substantially enhances model robustness and generalization while markedly reducing hyperparameter tuning overhead. This advancement improves the universality and deployment efficiency of federated learning systems, offering a practical pathway toward autonomous, scalable federated optimization.

Abstract

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.

Problem

• federated learning
• aggregation strategy
• non-IID
• automation
• heterogeneity

Innovation

• automated aggregation
• federated learning
• large language models
• genetic search
• non-IID