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Hierarchically Fair Federated Learning

arXiv 2004.10386 ↗First posted 22 Apr 2020cs.LG cs.AI cs.CYcited by 28

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Abstract (arXiv)

When the federated learning is adopted among competitive agents with siloed datasets, agents are self-interested and participate only if they are fairly rewarded. To encourage the application of federated learning, this paper employs a management strategy, i.e., more contributions should lead to more rewards. We propose a novel hierarchically fair federated learning (HFFL) framework. Under this framework, agents are rewarded in proportion to their pre-negotiated contribution levels. HFFL+ extends this to incorporate heterogeneous models. Theoretical analysis and empirical evaluation on several datasets confirm the efficacy of our frameworks in upholding fairness and thus facilitating federated learning in the competitive settings.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Apr 2020.

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Citing papers per year: 2021: 12; 2022: 1; 2023: 7; 2024: 5; 2025: 3.

28 papers.

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  1. 2021Towards Personalized Federated Learningcited by 464
  2. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
  3. 2021GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learningcited by 74
  4. 2021Towards Fairness-Aware Federated Learningcited by 58
  5. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  6. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 41
  7. 2021One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learningcited by 39
  8. 2021Improving Fairness via Federated Learningcited by 39
  9. 2021Prototype Guided Federated Learning of Visual Feature Representationscited by 22
  10. 2021Improving Fairness for Data Valuation in Horizontal Federated Learningcited by 22
  11. 2021GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learningcited by 21
  12. 2023Fairness-Aware Client Selection for Federated Learningcited by 15
16 more
  1. 2021Efficient Client Contribution Evaluation for Horizontal Federated Learningcited by 13
  2. 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Directioncited by 13
  3. 2021Models of fairness in federated learningcited by 8
  4. 2021Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learningcited by 7
  5. 2022FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Datacited by 6
  6. 2023FedDebug: Systematic Debugging for Federated Learning Applicationscited by 6
  7. 2025Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networkscited by 6
  8. 2024GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learningcited by 5
  9. 2024FedFair^3: Unlocking Threefold Fairness in Federated Learningcited by 5
  10. 2024Achieving Fairness Across Local and Global Models in Federated Learningcited by 5
  11. 2025Distributed Quasi-Newton Method for Fair and Fast Federated Learningcited by 5
  12. 2023Exploring Data Redundancy in Real-world Image Classification through Data Selectioncited by 3
  13. 2023Contribution Evaluation in Federated Learning: Examining Current Approachescited by 3
  14. 2023Hierarchical Federated Learning Incentivization for Gas Usage Estimationcited by 2
  15. 2024On the Volatility of Shapley-Based Contribution Metrics in Federated Learningcited by 2
  16. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0