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DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning

arXiv 2512.12022 ↗First posted 12 Dec 2025cs.LGcited by 0

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Abstract (arXiv)

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 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 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 35 of its 59 references that are in the map. First posted on arXiv 12 Dec 2025.

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35 of 59 references.

References · 35

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  4. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  5. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  6. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  7. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  8. 2019Agnostic Federated Learningcited by 456
  9. 2019Fair Resource Allocation in Federated Learningcited by 394
  10. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
  11. 2019A Little Is Enough: Circumventing Defenses For Distributed Learningcited by 288
  12. 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 187
23 more
  1. 2018Expanding the Reach of Federated Learning by Reducing Client Resource Requirementscited by 163
  2. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 118
  3. 2021Decentralized Federated Averagingcited by 114
  4. 2020Learning to Detect Malicious Clients for Robust Federated Learningcited by 88
  5. 2020Fairness-aware Agnostic Federated Learningcited by 87
  6. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  7. 2019Peer-to-peer Federated Learning on Graphscited by 85
  8. 2021Federated Learning with Fair Averagingcited by 61
  9. 2020Federated Learning Meets Multi-objective Optimizationcited by 53
  10. 2021Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysiscited by 51
  11. 2024Byzantine-Robust Decentralized Federated Learningcited by 48
  12. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 40
  13. 2023Benign Overfitting for Two-layer ReLU Convolutional Neural Networkscited by 34
  14. 2021SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networkscited by 30
  15. 2022Byzantine-Resilient Decentralized Stochastic Optimization with Robust Aggregation Rulescited by 23
  16. 2023Convergence Analysis of Sequential Federated Learning on Heterogeneous Datacited by 23
  17. 2021GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learningcited by 21
  18. 2021Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensuscited by 14
  19. 2023On the Tradeoff between Privacy Preservation and Byzantine-Robustness in Decentralized Learningcited by 8
  20. 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspectivecited by 4
  21. 2022MDA: Availability-Aware Federated Learning Client Selectioncited by 1
  22. 2025Performance Analysis of Decentralized Federated Learning Deploymentscited by 1
  23. 2025pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learningcited by 1

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