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arXiv · 2505.11648

Joint Graph Estimation and Signal Restoration for Robust Federated Learning

Tsutahiro Fukuhara·Junya Hara·Hiroshi Higashi·Yuichi Tanaka
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Abstract

We propose a robust aggregation method for model parameters in federated learning (FL) under noisy communications. FL is a distributed machine learning paradigm in which a central server aggregates local model parameters from multiple clients. These parameters are often noisy and/or have missing values during data collection, training, and communication between the clients and server. This may cause a considerable drop in model accuracy. To address this issue, we learn a graph that represents pairwise relationships between model parameters of the clients during aggregation. We realize it with a joint problem of graph learning and signal (i.e., model parameters) restoration. The problem is formulated as a difference-of-convex (DC) optimization, which is efficiently solved via a proximal DC algorithm. Experimental results on MNIST and CIFAR-10 datasets show that the proposed method outperforms existing approaches by up to $2$--$5\%$ in classification accuracy under biased data distributions and noisy conditions.

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Tsutahiro Fukuhara, Junya Hara, Hiroshi Higashi, Yuichi Tanaka. 2025-05-16. Joint Graph Estimation and Signal Restoration for Robust Federated Learning. https://arxiv.org/abs/2505.11648

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