Hardware-Sensitive Fairness in Heterogeneous Federated Learning

Federated Learning (FL) is a promising technique for decentralized privacy-preserving Machine Learning (ML) with a diverse pool of participating devices with varying device capabilities. However, existing approaches to handle such heterogeneous environments do not consider “fairness” in model aggregation, resulting in significant performance variation among devices. Meanwhile, prior works on FL fairness remain hardware-oblivious and cannot be applied directly without severe performance penalties. To address this issue, we propose a novel hardware-sensitive FL method called \(\mathsf {FairHetero}\) that promotes fairness among heterogeneous federated clients. Our approach offers tunable fairness within a group of devices with the same ML architecture as well as across different groups with heterogeneous models. Our evaluation under MNIST, FEMNIST, CIFAR10, and SHAKESPEARE datasets demonstrates that \(\mathsf {FairHetero}\) can reduce variance among participating clients’ test loss compared to the existing state-of-the-art (SOTA) techniques, resulting in increased overall performance.