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Variational Federated Multi-Task Learning

arXiv 1906.06268 ↗First posted 14 Jun 2019cs.LG stat.MLcited by 69

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Abstract (arXiv)

In federated learning, a central server coordinates the training of a single model on a massively distributed network of devices. This setting can be naturally extended to a multi-task learning framework, to handle real-world federated datasets that typically show strong statistical heterogeneity among devices. Despite federated multi-task learning being shown to be an effective paradigm for real-world datasets, it has been applied only on convex models. In this work, we introduce VIRTUAL, an algorithm for federated multi-task learning for general non-convex models. In VIRTUAL the federated network of the server and the clients is treated as a star-shaped Bayesian network, and learning is performed on the network using approximated variational inference. We show that this method is effective on real-world federated datasets, outperforming the current state-of-the-art for federated learning, and concurrently allowing sparser gradient updates.

Key facts. Cited by 69 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Jun 2019.

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Citing papers per year: 2020: 1; 2021: 22; 2022: 10; 2023: 16; 2024: 9; 2025: 7; 2026: 4.

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  1. 2021Towards Personalized Federated Learningcited by 464
  2. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  3. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  4. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  5. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  6. 2024Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resourcescited by 93
  7. 2021Federated Evaluation and Tuning for On-Device Personalization: System Design & Applicationscited by 85
  8. 2021Personalized Federated Learning with Gaussian Processescited by 76
  9. 2021Towards Fair Federated Learning with Zero-Shot Data Augmentationcited by 64
  10. 2021Personalized Federated Learning through Local Memorizationcited by 57
  11. 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
  12. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 51
38 more
  1. 2021Cluster-driven Graph Federated Learning over Multiple Domainscited by 44
  2. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  3. 2021A Bayesian Federated Learning Framework with Online Laplace Approximationcited by 41
  4. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 41
  5. 2023Efficient Personalized Federated Learning via Sparse Model-Adaptationcited by 38
  6. 2023Personalized Federated Learning under Mixture of Distributionscited by 37
  7. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  8. 2022FedPop: A Bayesian Approach for Personalised Federated Learningcited by 35
  9. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  10. 2021QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learningcited by 26
  11. 2023Bayesian Federated Learning: A Surveycited by 22
  12. 2020PFL-MoE: Personalized Federated Learning Based on Mixture of Expertscited by 21
  13. 2021Architectural Patterns for the Design of Federated Learning Systemscited by 17
  14. 2021Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Trainingcited by 17
  15. 2023Federated Learning as Variational Inference: A Scalable Expectation Propagation Approachcited by 17
  16. 2023Confidence-aware Personalized Federated Learning via Variational Expectation Maximizationcited by 17
  17. 2021On Bridging Generic and Personalized Federated Learning for Image Classificationcited by 15
  18. 2024Federated Distillation: A Surveycited by 15
  19. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  20. 2021Failure Prediction in Production Line Based on Federated Learning: An Empirical Studycited by 11
  21. 2023Personalized Online Federated Learning with Multiple Kernelscited by 11
  22. 2022Find Your Friends: Personalized Federated Learning with the Right Collaboratorscited by 10
  23. 2021An Expectation-Maximization Perspective on Federated Learningcited by 9
  24. 2022FedDAR: Federated Domain-Aware Representation Learningcited by 8
  25. 2022Personalized Federated Learning via Heterogeneous Modular Networkscited by 8
  26. 2023Time-sensitive Learning for Heterogeneous Federated Edge Intelligencecited by 8
  27. 2023Federated Learning of Shareable Bases for Personalization-Friendly Image Classificationcited by 6
  28. 2021IFedAvg: Interpretable Data-Interoperability for Federated Learningcited by 5
  29. 2021Federated Learning with Heterogeneous Differential Privacycited by 5
  30. 2024Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approachcited by 5
  31. 2021Implicit Model Specialization through DAG-based Decentralized Federated Learningcited by 4
  32. 2022Client Selection for Federated Bayesian Learningcited by 4
  33. 2022Graph Federated Learning for CIoT Devices in Smart Home Applicationscited by 4
  34. 2024A Bayesian Framework for Clustered Federated Learningcited by 3
  35. 2022Multi-Task and Transfer Learning for Federated Learning Applicationscited by 2
  36. 2023Federated Variational Inference: Towards Improved Personalization and Generalizationcited by 2
  37. 2025Personalized Federated Learning via Learning Dynamic Graphscited by 2
  38. 2021Demystifying the Effects of Non-Independence in Federated Learningcited by 1

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