Roughness-Informed Federated Learning

• URL: http://arxiv.org/abs/2602.10595v1
• Date: Wed, 11 Feb 2026 07:35:45 GMT
• Title: Roughness-Informed Federated Learning
• Authors: Mohammad Partohaghighi, Roummel Marcia, Bruce J. West, YangQuan Chen,
• Abstract summary: Federated Learning (FL) enables collaborative model training across distributed clients.<n>FL faces challenges in non-independent and identically distributed (non-IID) settings due to client drift.<n>We propose RI-FedAvg, a novel FL that mitigates client drift by incorporating a Roughness Index (RI)-based regularization term.
• Score: 3.8218584696400484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, yet faces challenges in non-independent and identically distributed (non-IID) settings due to client drift, which impairs convergence. We propose RI-FedAvg, a novel FL algorithm that mitigates client drift by incorporating a Roughness Index (RI)-based regularization term into the local objective, adaptively penalizing updates based on the fluctuations of local loss landscapes. This paper introduces RI-FedAvg, leveraging the RI to quantify the roughness of high-dimensional loss functions, ensuring robust optimization in heterogeneous settings. We provide a rigorous convergence analysis for non-convex objectives, establishing that RI-FedAvg converges to a stationary point under standard assumptions. Extensive experiments on MNIST, CIFAR-10, and CIFAR-100 demonstrate that RI-FedAvg outperforms state-of-the-art baselines, including FedAvg, FedProx, FedDyn, SCAFFOLD, and DP-FedAvg, achieving higher accuracy and faster convergence in non-IID scenarios. Our results highlight RI-FedAvg's potential to enhance the robustness and efficiency of federated learning in practical, heterogeneous environments.

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