Stabilizing Federated Learning under Extreme Heterogeneity with HeteRo-Select
This provides a reliable solution for real-world heterogeneous FL problems, though it is incremental as it builds on existing client selection methods.
The paper tackles training instability in Federated Learning under extreme data heterogeneity by proposing the HeteRo-Select framework, which achieves a peak accuracy of 74.75%, final accuracy of 72.76%, and minimal stability drop of 1.99% on CIFAR-10 with label skew, outperforming Oort.
Federated Learning (FL) is a machine learning technique that often suffers from training instability due to the diverse nature of client data. Although utility-based client selection methods like Oort are used to converge by prioritizing high-loss clients, they frequently experience significant drops in accuracy during later stages of training. We propose a theoretical HeteRo-Select framework designed to maintain high performance and ensure long-term training stability. We provide a theoretical analysis showing that when client data is very different (high heterogeneity), choosing a smart subset of client participation can reduce communication more effectively compared to full participation. Our HeteRo-Select method uses a clear, step-by-step scoring system that considers client usefulness, fairness, update speed, and data variety. It also shows convergence guarantees under strong regularization. Our experimental results on the CIFAR-10 dataset under significant label skew ($α=0.1$) support the theoretical findings. The HeteRo-Select method performs better than existing approaches in terms of peak accuracy, final accuracy, and training stability. Specifically, HeteRo-Select achieves a peak accuracy of $74.75\%$, a final accuracy of $72.76\%$, and a minimal stability drop of $1.99\%$. In contrast, Oort records a lower peak accuracy of $73.98\%$, a final accuracy of $71.25\%$, and a larger stability drop of $2.73\%$. The theoretical foundations and empirical performance in our study make HeteRo-Select a reliable solution for real-world heterogeneous FL problems.