Collaborative Fairness in Federated Learning
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Abstract (arXiv)
In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability. This problem can be addressed by Distributed or Federated Learning (FL) that leverages a parameter server to aggregate model updates from individual participants. However, most existing Distributed or FL frameworks have overlooked an important aspect of participation: collaborative fairness. In particular, all participants can receive the same or similar models, regardless of their contributions. To address this issue, we investigate the collaborative fairness in FL, and propose a novel Collaborative Fair Federated Learning (CFFL) framework which utilizes reputation to enforce participants to converge to different models, thus achieving fairness without compromising the predictive performance. Extensive experiments on benchmark datasets demonstrate that CFFL achieves high fairness, delivers comparable accuracy to the Distributed framework, and outperforms the Standalone framework.
Key facts. Cited by 95 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Aug 2020.
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Citing papers per year: 2021: 15; 2022: 8; 2023: 22; 2024: 31; 2025: 14; 2026: 5.
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- 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
- 2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
- 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
- 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 83
- 2021GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learningcited by 73
- 2021Federated Learning with Fair Averagingcited by 59
- 2021Towards Fairness-Aware Federated Learningcited by 57
- 2023Fair Federated Medical Image Segmentation via Client Contribution Estimationcited by 50
- 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
- 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
- 2021One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learningcited by 39
- 2021Improving Fairness via Federated Learningcited by 39
38 more
- 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
- 2024Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversitycited by 28
- 2022Minimax Demographic Group Fairness in Federated Learningcited by 24
- 2021Improving Fairness for Data Valuation in Horizontal Federated Learningcited by 22
- 2021GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learningcited by 21
- 2021Incentivizing Collaboration in Machine Learning via Synthetic Data Rewardscited by 20
- 2022Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentationcited by 16
- 2024Redefining Contributions: Shapley-Driven Federated Learningcited by 16
- 2023IronForge: An Open, Secure, Fair, Decentralized Federated Learningcited by 15
- 2021FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learningcited by 14
- 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
- 2023DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuationcited by 12
- 2023Bias Propagation in Federated Learningcited by 12
- 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Directioncited by 12
- 2021MarS-FL: Enabling Competitors to Collaborate in Federated Learningcited by 11
- 2025FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Modelscited by 11
- 2023Federated Recommendation with Additive Personalizationcited by 9
- 2025Stochastic Self-Organization in Multi-Agent Systemscited by 9
- 2021Loss Tolerant Federated Learningcited by 8
- 2021Models of fairness in federated learningcited by 8
- 2023Towards Fair Graph Federated Learning via Incentive Mechanismscited by 8
- 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learningcited by 8
- 2023FedDebug: Systematic Debugging for Federated Learning Applicationscited by 6
- 2023Strategic Data Sharing between Competitorscited by 6
- 2025Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networkscited by 6
- 2022Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurementcited by 5
- 2023Mitigating Group Bias in Federated Learning for Heterogeneous Devicescited by 5
- 2024GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learningcited by 5
- 2024FedFair^3: Unlocking Threefold Fairness in Federated Learningcited by 5
- 2024PraFFL: A Preference-Aware Scheme in Fair Federated Learningcited by 5
- 2024From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matchingcited by 5
- 2024ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learningcited by 5
- 2024Achieving Fairness Across Local and Global Models in Federated Learningcited by 5
- 2022Towards Fairness-Aware Multi-Objective Optimizationcited by 4
- 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
- 2024Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processingcited by 4
- 2024FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learningcited by 4
- 2024Federated Fairness Analytics: Quantifying Fairness in Federated Learningcited by 4
50 most cited of 95 All 95 (JSON)
- 2026FedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classification4 Sep 2026
- 2026Class-wise Contribution Estimation via Logit Maximization for Federated Learning17 May 2026
- 2026Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy24 Apr 2026
- 2026SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version)26 Feb 2026
- 2026Federated Learning at the Forefront of Fairness: A Multifaceted Perspective31 Jan 2026
- 2025FeDABoost: Fairness Aware Federated Learning with Adaptive Boosting3 Oct 2025
- 2025Stochastic Self-Organization in Multi-Agent Systems1 Oct 2025
- 2025Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift11 Jul 2025
- 2025FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models3 Jun 2025
- 2025Fairness in Federated Learning: Fairness for Whom?27 May 2025
- 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoning22 May 2025
- 2025Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning30 Apr 2025
38 more
- 2025Achieving Distributive Justice in Federated Learning via Uncertainty Quantification22 Apr 2025
- 2025Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients17 Apr 2025
- 2025Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching12 Mar 2025
- 2025MAB-Based Channel Scheduling for Asynchronous Federated Learning in Non-Stationary Environments3 Mar 2025
- 2025Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks7 Feb 2025
- 2025CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning21 Jan 2025
- 2025Distributed Quasi-Newton Method for Fair and Fast Federated Learning18 Jan 2025
- 2024WassFFed: Wasserstein Fair Federated Learning11 Nov 2024
- 2024Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning5 Nov 2024
- 2024Equitable Federated Learning with Activation Clustering24 Oct 2024
- 2024A New Perspective to Boost Performance Fairness for Medical Federated Learning12 Oct 2024
- 2024Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentives10 Oct 2024
- 2024RAB2-DEF: Dynamic and explainable defense against adversarial attacks in Federated Learning to fair poor clients10 Oct 2024
- 2024Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning9 Oct 2024
- 2024FRIDA: Free-Rider Detection using Privacy Attacks7 Oct 2024
- 2024EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning2 Oct 2024
- 2024Federated Fairness Analytics: Quantifying Fairness in Federated Learning15 Aug 2024
- 2024Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering27 Jul 2024
- 2024On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness23 Jul 2024
- 2024Privacy-preserving gradient-based fair federated learning18 Jul 2024
- 2024FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging8 Jul 2024
- 2024Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representations2 Jul 2024
- 2024Achieving Fairness Across Local and Global Models in Federated Learning24 Jun 2024
- 2024Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives16 Jun 2024
- 2024Redefining Contributions: Shapley-Driven Federated Learning1 Jun 2024
- 2024ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning31 May 2024
- 2024FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning28 May 2024
- 2024Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing28 May 2024
- 2024Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity26 May 2024
- 2024FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?22 May 2024
- 2024From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching27 Apr 2024
- 2024PraFFL: A Preference-Aware Scheme in Fair Federated Learning13 Apr 2024
- 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning28 Mar 2024
- 2024History-Aware and Dynamic Client Contribution in Federated Learning11 Mar 2024
- 2024Distribution-Free Fair Federated Learning with Small Samples25 Feb 2024
- 2024FedFair^3: Unlocking Threefold Fairness in Federated Learning29 Jan 2024
- 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Direction10 Jan 2024
- 2024GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning7 Jan 2024
50 newest of 95 All 95 (JSON)
- 2025Stochastic Self-Organization in Multi-Agent Systemscited by 9
- 2025Stochastic Self-Organization in Multi-Agent Systems1 Oct 2025
- 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
- 2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
- 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
- 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 83
- 2021GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learningcited by 73
- 2021Federated Learning with Fair Averagingcited by 59
- 2021Towards Fairness-Aware Federated Learningcited by 57
- 2023Fair Federated Medical Image Segmentation via Client Contribution Estimationcited by 50
- 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
- 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
- 2021One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learningcited by 39
- 2021Improving Fairness via Federated Learningcited by 39
12 most cited of 94 All 94 (JSON)
- 2026FedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classification4 Sep 2026
- 2026Class-wise Contribution Estimation via Logit Maximization for Federated Learning17 May 2026
- 2026Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy24 Apr 2026
- 2026SettleFL: Trustless and Scalable Reward Settlement Protocol for Federated Learning on Permissionless Blockchains (Extended version)26 Feb 2026
- 2026Federated Learning at the Forefront of Fairness: A Multifaceted Perspective31 Jan 2026
- 2025FeDABoost: Fairness Aware Federated Learning with Adaptive Boosting3 Oct 2025
- 2025Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift11 Jul 2025
- 2025FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models3 Jun 2025
- 2025Fairness in Federated Learning: Fairness for Whom?27 May 2025
- 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoning22 May 2025
- 2025Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning30 Apr 2025
- 2025Achieving Distributive Justice in Federated Learning via Uncertainty Quantification22 Apr 2025
12 newest of 94 All 94 (JSON)