{"ID":2829990,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.12022","arxiv_id":"2512.12022","title":"DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning","abstract":"Decentralized federated learning (DFL) has emerged as a promising paradigm that enables multiple clients to collaboratively train machine learning models through iterative rounds of local training, communication, and aggregation, without relying on a central server. Nevertheless, DFL systems continue to face a range of challenges, including fairness and Byantine robustness. To address these challenges, we propose \\textbf{DFedReweighting}, a unified aggregation framework that achieves diverse learning objectives in DFL via objective-oriented reweighting at the final step of each learning round. Specifically, for each client, the framework first evaluates a target performance metric (TPM) on a compact auxiliary dataset constructed from local data, yielding preliminary aggregation weights, which are subsequently refined by a customized reweighting strategy (CRS) to produce the final aggregation weights. Theoretically, we prove that an appropriate TPM-CRS combination guarantees linear convergence for general $L$-smoothand strongly convex functions. Empirical results consistently demonstrate that \\textbf{DFedReweighting} significantly improves fairness and robustness against Byzantine attacks across diverse settings. Two multi-objective examples, spanning tasks across and within clients, further establish that a broad range of desired learning objectives can be accommodated by appropriately designing the TPM and CRS. Our code is available at https://github.com/KaichuangZhang/DFedReweighting.","short_abstract":"Decentralized federated learning (DFL) has emerged as a promising paradigm that enables multiple clients to collaboratively train machine learning models through iterative rounds of local training, communication, and aggregation, without relying on a central server. Nevertheless, DFL systems continue to face a range of...","url_abs":"https://arxiv.org/abs/2512.12022","url_pdf":"https://arxiv.org/pdf/2512.12022v2","authors":"[\"Kaichuang Zhang\",\"Wei Yin\",\"Jinghao Yang\",\"Ping Xu\"]","published":"2025-12-12T20:30:11Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":605988,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2829990,"paper_url":"https://arxiv.org/abs/2512.12022","paper_title":"DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning","repo_url":"https://github.com/KaichuangZhang/DFedReweighting","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}