Joint Graph Estimation and Signal Restoration for Robust Federated Learning
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913844143587328 |
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| author | Fukuhara, Tsutahiro Hara, Junya Higashi, Hiroshi Tanaka, Yuichi |
| author_facet | Fukuhara, Tsutahiro Hara, Junya Higashi, Hiroshi Tanaka, Yuichi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11648 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Graph Estimation and Signal Restoration for Robust Federated Learning Fukuhara, Tsutahiro Hara, Junya Higashi, Hiroshi Tanaka, Yuichi Machine Learning Signal Processing 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. |
| title | Joint Graph Estimation and Signal Restoration for Robust Federated Learning |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2505.11648 |