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

arXiv: https://arxiv.org/abs/2512.12022 · HTML: https://mlsanity.com/paper/2512.12022/

## 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.

## Builds on (most cited first, 30 of 35)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [A Little Is Enough: Circumventing Defenses For Distributed Learning](https://mlsanity.com/paper/1902.06156/) (2019)
- [FedKD: Communication Efficient Federated Learning via Knowledge Distillation](https://mlsanity.com/paper/2108.13323/) (2021)
- [Expanding the Reach of Federated Learning by Reducing Client Resource Requirements](https://mlsanity.com/paper/1812.07210/) (2018)
- [FairFed: Enabling Group Fairness in Federated Learning](https://mlsanity.com/paper/2110.00857/) (2021)
- [Decentralized Federated Averaging](https://mlsanity.com/paper/2104.11375/) (2021)
- [Learning to Detect Malicious Clients for Robust Federated Learning](https://mlsanity.com/paper/2002.00211/) (2020)
- [Fairness-aware Agnostic Federated Learning](https://mlsanity.com/paper/2010.05057/) (2020)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Peer-to-peer Federated Learning on Graphs](https://mlsanity.com/paper/1901.11173/) (2019)
- [Federated Learning with Fair Averaging](https://mlsanity.com/paper/2104.14937/) (2021)
- [Federated Learning Meets Multi-objective Optimization](https://mlsanity.com/paper/2006.11489/) (2020)
- [Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis](https://mlsanity.com/paper/2101.12704/) (2021)
- [Byzantine-Robust Decentralized Federated Learning](https://mlsanity.com/paper/2406.10416/) (2024)
- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [Benign Overfitting for Two-layer ReLU Convolutional Neural Networks](https://mlsanity.com/paper/2303.04145/) (2023)
- [SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks](https://mlsanity.com/paper/2106.02743/) (2021)
- [Byzantine-Resilient Decentralized Stochastic Optimization with Robust Aggregation Rules](https://mlsanity.com/paper/2206.04568/) (2022)
- [Convergence Analysis of Sequential Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2311.03154/) (2023)
- [GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning](https://mlsanity.com/paper/2108.02741/) (2021)
- [Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus](https://mlsanity.com/paper/2101.03300/) (2021)

## 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.