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Federated Multi-Task Learning

arXiv 1705.10467 ↗First posted 30 May 2017cs.LG stat.MLcited by 669

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Abstract (arXiv)

Federated learning poses new statistical and systems challenges in training machine learning models over distributed networks of devices. In this work, we show that multi-task learning is naturally suited to handle the statistical challenges of this setting, and propose a novel systems-aware optimization method, MOCHA, that is robust to practical systems issues. Our method and theory for the first time consider issues of high communication cost, stragglers, and fault tolerance for distributed multi-task learning. The resulting method achieves significant speedups compared to alternatives in the federated setting, as we demonstrate through simulations on real-world federated datasets.

Key facts. Cited by 669 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 May 2017.

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Citing papers per year: 2020: 1; 2021: 181; 2022: 129; 2023: 113; 2024: 101; 2025: 100; 2026: 44.

669 papers.

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  1. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  2. 2021Towards Personalized Federated Learningcited by 462
  3. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  4. 2021A Field Guide to Federated Optimizationcited by 312
  5. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  6. 2021Personalized Federated Learning using Hypernetworkscited by 210
  7. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  8. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  9. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 180
  10. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  11. 2021Advances in adversarial attacks and defenses in computer vision: A surveycited by 127
  12. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
38 more
  1. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 124
  2. 2022Federated Learning with Partial Model Personalizationcited by 114
  3. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  4. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 108
  5. 2022Improving Generalization in Federated Learning by Seeking Flat Minimacited by 98
  6. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  7. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  8. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  9. 2021CRFL: Certifiably Robust Federated Learning against Backdoor Attackscited by 91
  10. 2023Deep Model Fusion: A Surveycited by 89
  11. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  12. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 84
  13. 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 84
  14. 2021Federated Reconstruction: Partially Local Federated Learningcited by 78
  15. 2023Federated Learning for Medical Image Analysis: A Surveycited by 77
  16. 2021Personalized Federated Learning with Gaussian Processescited by 76
  17. 2021Fast Federated Learning in the Presence of Arbitrary Device Unavailabilitycited by 72
  18. 2021Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modelingcited by 69
  19. 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
  20. 2021FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learningcited by 67
  21. 2024Learning to Route Among Specialized Experts for Zero-Shot Generalizationcited by 65
  22. 2021Towards Fair Federated Learning with Zero-Shot Data Augmentationcited by 64
  23. 2021Federated Multi-armed Bandits with Personalizationcited by 62
  24. 2021What Do We Mean by Generalization in Federated Learning?cited by 62
  25. 2021Provably Secure Federated Learning against Malicious Clientscited by 60
  26. 2021Federated Multi-Armed Banditscited by 59
  27. 2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
  28. 2021Personalized Federated Learning through Local Memorizationcited by 57
  29. 2021Decentralized Federated Learning: Balancing Communication and Computing Costscited by 54
  30. 2021Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learningcited by 53
  31. 2022FederatedScope: A Flexible Federated Learning Platform for Heterogeneitycited by 53
  32. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 50
  33. 2021FedGEMS: Federated Learning of Larger Server Models via Selective Knowledge Fusioncited by 49
  34. 2021Flexible Clustered Federated Learning for Client-Level Data Distribution Shiftcited by 47
  35. 2021FedSoft: Soft Clustered Federated Learning with Proximal Local Updatingcited by 46
  36. 2022Orchestra: Unsupervised Federated Learning via Globally Consistent Clusteringcited by 45
  37. 2022Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillationcited by 45
  38. 2021Federated Multi-Target Domain Adaptationcited by 44

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