mlsanityData to 7 Oct 2026

Federated Learning Meets Multi-objective Optimization

arXiv 2006.11489 ↗First posted 20 Jun 2020cs.LG stat.MLcited by 50

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Abstract (arXiv)

Federated learning has emerged as a promising, massively distributed way to train a joint deep model over large amounts of edge devices while keeping private user data strictly on device. In this work, motivated from ensuring fairness among users and robustness against malicious adversaries, we formulate federated learning as multi-objective optimization and propose a new algorithm FedMGDA+ that is guaranteed to converge to Pareto stationary solutions. FedMGDA+ is simple to implement, has fewer hyperparameters to tune, and refrains from sacrificing the performance of any participating user. We establish the convergence properties of FedMGDA+ and point out its connections to existing approaches. Extensive experiments on a variety of datasets confirm that FedMGDA+ compares favorably against state-of-the-art.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 5; 2023: 10; 2024: 11; 2025: 13; 2026: 6.

50 papers.

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  1. 2021A Field Guide to Federated Optimizationcited by 312
  2. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
  3. 2021Federated Learning with Fair Averagingcited by 59
  4. 2021Towards Fairness-Aware Federated Learningcited by 57
  5. 2023Fair Federated Medical Image Segmentation via Client Contribution Estimationcited by 50
  6. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
  7. 2021Enforcing fairness in private federated learning via the modified method of differential multiplierscited by 38
  8. 2022Proportional Fairness in Federated Learningcited by 20
  9. 2022One-shot Federated Learning without Server-side Trainingcited by 20
  10. 2023FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attackscited by 15
  11. 2025Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyondcited by 15
  12. 2023Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learningcited by 12
38 more
  1. 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Directioncited by 12
  2. 2024Rethinking the Representation in Federated Unsupervised Learning with Non-IID Datacited by 12
  3. 2023Federated Multi-Objective Learningcited by 11
  4. 2024FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfercited by 9
  5. 2023Accelerating Fair Federated Learning: Adaptive Federated Adamcited by 8
  6. 2025Federated Learning with Domain Shift Erasercited by 7
  7. 2024Many-Objective Multi-Solution Transportcited by 5
  8. 2025When Federated Learning Meets Quantum Computing: Survey and Research Opportunitiescited by 5
  9. 2023Entropy-driven Fair and Effective Federated Learningcited by 4
  10. 2024Adaptive Online Mirror Descent for Tchebycheff Scalarization in Multi-Objective Learningcited by 4
  11. 2025Distributed Quasi-Newton Method for Fair and Fast Federated Learningcited by 4
  12. 2023Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Searchcited by 3
  13. 2024Federated Communication-Efficient Multi-Objective Optimizationcited by 3
  14. 2024FedDP: Privacy-preserving method based on federated learning for histopathology image segmentationcited by 3
  15. 2025Over-the-Air Fair Federated Learning via Multi-Objective Optimizationcited by 3
  16. 2025Multi-objective methods in Federated Learning: A survey and taxonomycited by 3
  17. 2024Federated Fairness without Access to Sensitive Groupscited by 2
  18. 2024Pursuing Overall Welfare in Federated Learning through Sequential Decision Makingcited by 2
  19. 2024A New Perspective to Boost Performance Fairness for Medical Federated Learningcited by 2
  20. 2024Boosting Fairness and Robustness in Over-the-Air Federated Learningcited by 1
  21. 2025Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Schedulingcited by 1
  22. 2022Efficient Adaptive Federated Optimization of Federated Learning for IoTcited by 0
  23. 2022Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and Fusioncited by 0
  24. 2022Robust Distributed Learning Against Both Distributional Shifts and Byzantine Attackscited by 0
  25. 2023Hierarchical and Decentralised Federated Learningcited by 0
  26. 2023The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learningcited by 0
  27. 2025FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learningcited by 0
  28. 2025Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learningcited by 0
  29. 2025Federated Multi-Objective Learning with Controlled Pareto Frontierscited by 0
  30. 2025FAIRVAR: Fair Federated Learning via Variance Regularizationcited by 0
  31. 2025Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learningcited by 0
  32. 2025DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learningcited by 0
  33. 2026Few-for-Many Personalized Federated Learningcited by 0
  34. 2026Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMscited by 0
  35. 2026Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networkscited by 0
  36. 2026PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Taskscited by 0
  37. 2026CRAFT: Conflict-Resolved Aggregation for Federated Trainingcited by 0
  38. 2026Efficient Hessian-Free Methods for Multi-Objective Bilevel Optimization with Nonconvex Lower Levelcited by 0