mlsanityData to 14 Oct 2026

FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning

arXiv 2405.18291 ↗First posted 28 May 2024cs.LG cs.AI cs.DCcited by 4

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Abstract (arXiv)

Collaborative fairness stands as an essential element in federated learning to encourage client participation by equitably distributing rewards based on individual contributions. Existing methods primarily focus on adjusting gradient allocations among clients to achieve collaborative fairness. However, they frequently overlook crucial factors such as maintaining consistency across local models and catering to the diverse requirements of high-contributing clients. This oversight inevitably decreases both fairness and model accuracy in practice. To address these issues, we propose FedSAC, a novel Federated learning framework with dynamic Submodel Allocation for Collaborative fairness, backed by a theoretical convergence guarantee. First, we present the concept of "bounded collaborative fairness (BCF)", which ensures fairness by tailoring rewards to individual clients based on their contributions. Second, to implement the BCF, we design a submodel allocation module with a theoretical guarantee of fairness. This module incentivizes high-contributing clients with high-performance submodels containing a diverse range of crucial neurons, thereby preserving consistency across local models. Third, we further develop a dynamic aggregation module to adaptively aggregate submodels, ensuring the equitable treatment of low-frequency neurons and consequently enhancing overall model accuracy. Extensive experiments conducted on three public benchmarks demonstrate that FedSAC outperforms all baseline methods in both fairness and model accuracy. We see this work as a significant step towards incentivizing broader client participation in federated learning. The source code is available at https://github.com/wangzihuixmu/FedSAC.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 26 of its 54 references that are in the map. First posted on arXiv 28 May 2024.

Builds on

26 of 54 references.

References · 26

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  4. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  5. 2018Local SGD Converges Fast and Communicates Littlecited by 605
  6. 2017ThiNet: A Filter Level Pruning Method for Deep Neural Network Compressioncited by 442
  7. 2019Fair Resource Allocation in Federated Learningcited by 394
  8. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 387
  9. 2018Sparsified SGD with Memorycited by 376
  10. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  11. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 278
  12. 2017NISP: Pruning Networks using Neuron Importance Score Propagationcited by 228
14 more
  1. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  2. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
  3. 2020A Principled Approach to Data Valuation for Federated Learningcited by 127
  4. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  5. 2021Local Learning Matters: Rethinking Data Heterogeneity in Federated Learningcited by 114
  6. 2020Collaborative Fairness in Federated Learningcited by 95
  7. 2022Federated Learning on Non-IID Graphs via Structural Knowledge Sharingcited by 88
  8. 2018A Marketplace for Data: An Algorithmic Solutioncited by 76
  9. 2020Collaborative Machine Learning with Incentive-Aware Model Rewardscited by 71
  10. 2020A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learningcited by 41
  11. 2022Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customizationcited by 35
  12. 2022TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernelscited by 26
  13. 2021Incentivizing Collaboration in Machine Learning via Synthetic Data Rewardscited by 20
  14. 2020How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learningcited by 12

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shiftcited by 2
  2. 2025FeDABoost: Fairness Aware Federated Learning with Adaptive Boostingcited by 1
  3. 2026CoRe-Fed: Bridging Collaborative and Representation Fairness via Federated Embedding Distillationcited by 0
  4. 2026From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learningcited by 0