mlsanityData to 14 Oct 2026

Fair Resource Allocation in Federated Learning

arXiv 1905.10497 ↗First posted 25 May 2019cs.LG stat.MLcited by 394

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Abstract (arXiv)

Federated learning involves training statistical models in massive, heterogeneous networks. Naively minimizing an aggregate loss function in such a network may disproportionately advantage or disadvantage some of the devices. In this work, we propose q-Fair Federated Learning (q-FFL), a novel optimization objective inspired by fair resource allocation in wireless networks that encourages a more fair (specifically, a more uniform) accuracy distribution across devices in federated networks. To solve q-FFL, we devise a communication-efficient method, q-FedAvg, that is suited to federated networks. We validate both the effectiveness of q-FFL and the efficiency of q-FedAvg on a suite of federated datasets with both convex and non-convex models, and show that q-FFL (along with q-FedAvg) outperforms existing baselines in terms of the resulting fairness, flexibility, and efficiency.

Key facts. Cited by 394 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 May 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 90; 2022: 76; 2023: 62; 2024: 77; 2025: 53; 2026: 36.

394 papers.

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  1. 2021Towards Personalized Federated Learningcited by 464
  2. 2021A Field Guide to Federated Optimizationcited by 315
  3. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  4. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  5. 2022Generalized Federated Learning via Sharpness Aware Minimizationcited by 144
  6. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 142
  7. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  8. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 128
  9. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 118
  10. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 109
  11. 2021Distributionally Robust Federated Averagingcited by 104
  12. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
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  1. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  2. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  3. 2021On Large-Cohort Training for Federated Learningcited by 74
  4. 2023FedDisco: Federated Learning with Discrepancy-Aware Collaborationcited by 67
  5. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 66
  6. 2021Towards Fair Federated Learning with Zero-Shot Data Augmentationcited by 64
  7. 2021Provably Secure Federated Learning against Malicious Clientscited by 63
  8. 2021What Do We Mean by Generalization in Federated Learning?cited by 62
  9. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  10. 2021Federated Learning with Fair Averagingcited by 61
  11. 2021Federated Multi-Armed Banditscited by 59
  12. 2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
  13. 2021Towards Fairness-Aware Federated Learningcited by 58
  14. 2021FedLab: A Flexible Federated Learning Frameworkcited by 55
  15. 2022Communication-Efficient Adaptive Federated Learningcited by 55
  16. 2021Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learningcited by 52
  17. 2023Fair Federated Medical Image Segmentation via Client Contribution Estimationcited by 52
  18. 2021Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learningcited by 50
  19. 2021FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusioncited by 49
  20. 2022FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clientscited by 46
  21. 2022Orchestra: Unsupervised Federated Learning via Globally Consistent Clusteringcited by 45
  22. 2022Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillationcited by 45
  23. 2021Unsupervised Learning of Debiased Representations with Pseudo-Attributescited by 44
  24. 2021On Tilted Losses in Machine Learning: Theory and Applicationscited by 43
  25. 2021Anarchic Federated Learningcited by 42
  26. 2022Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentationcited by 42
  27. 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
  28. 2021Optimality and Stability in Federated Learning: A Game-theoretic Approachcited by 40
  29. 2021One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learningcited by 39
  30. 2021AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learningcited by 39
  31. 2021Improving Fairness via Federated Learningcited by 39
  32. 2022Test-Time Robust Personalization for Federated Learningcited by 39
  33. 2021Enforcing fairness in private federated learning via the modified method of differential multiplierscited by 38
  34. 2021Scaling Federated Learning for Fine-tuning of Large Language Modelscited by 36
  35. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  36. 2024Fair Resource Allocation in Multi-Task Learningcited by 35
  37. 2021FedJAX: Federated learning simulation with JAXcited by 32
  38. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32

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