mlsanityData to 14 Oct 2026

PeFLL: Personalized Federated Learning by Learning to Learn

arXiv 2306.05515 ↗First posted 8 Jun 2023cs.LGcited by 21

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Abstract (arXiv)

We present PeFLL, a new personalized federated learning algorithm that improves over the state-of-the-art in three aspects: 1) it produces more accurate models, especially in the low-data regime, and not only for clients present during its training phase, but also for any that may emerge in the future; 2) it reduces the amount of on-client computation and client-server communication by providing future clients with ready-to-use personalized models that require no additional finetuning or optimization; 3) it comes with theoretical guarantees that establish generalization from the observed clients to future ones. At the core of PeFLL lies a learning-to-learn approach that jointly trains an embedding network and a hypernetwork. The embedding network is used to represent clients in a latent descriptor space in a way that reflects their similarity to each other. The hypernetwork takes as input such descriptors and outputs the parameters of fully personalized client models. In combination, both networks constitute a learning algorithm that achieves state-of-the-art performance in several personalized federated learning benchmarks.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 37 of its 63 references that are in the map. First posted on arXiv 8 Jun 2023.

Builds on

37 of 63 references.

References · 37

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  5. 2017Deep Setscited by 1,421
  6. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
  7. 2016HyperNetworkscited by 1,027
  8. 2020Meta-Learning in Neural Networks: A Surveycited by 950
  9. 2018LEAF: A Benchmark for Federated Settingscited by 792
  10. 2017Federated Multi-Task Learningcited by 672
  11. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  12. 2016Learning to learn by gradient descent by gradient descentcited by 620
25 more
  1. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  2. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  3. 2019Federated Learning with Personalization Layerscited by 541
  4. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  5. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  6. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
  7. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  8. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  9. 2018Distributed learning of deep neural network over multiple agentscited by 279
  10. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 269
  11. 2021Personalized Federated Learning using Hypernetworkscited by 212
  12. 2020Federated Learning of a Mixture of Global and Local Modelscited by 210
  13. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  14. 2020Lower Bounds and Optimal Algorithms for Personalized Federated Learningcited by 123
  15. 2020Tighter risk certificates for neural networkscited by 112
  16. 2022Layer-wised Model Aggregation for Personalized Federated Learningcited by 96
  17. 2020PACOH: Bayes-Optimal Meta-Learning with PAC-Guaranteescited by 85
  18. 2017Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theorycited by 76
  19. 2013A PAC-Bayesian bound for Lifelong Learningcited by 69
  20. 2021Personalized Federated Learning through Local Memorizationcited by 57
  21. 2023Efficient Personalized Federated Learning via Sparse Model-Adaptationcited by 38
  22. 2023Personalized Federated Learning under Mixture of Distributionscited by 37
  23. 2022FedTP: Federated Learning by Transformer Personalizationcited by 36
  24. 2019Federated User Representation Learningcited by 32
  25. 2021A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularizationcited by 27

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 12; 2026: 5.

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  1. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  2. 2024More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithmscited by 11
  3. 2025From Low Intrinsic Dimensionality to Non-Vacuous Generalization Bounds in Deep Multi-Task Learningcited by 5
  4. 2024FedCoSR: Personalized Federated Learning with Contrastive Shareable Representations for Label Heterogeneity in Non-IID Datacited by 3
  5. 2024Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomialscited by 3
  6. 2025Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learningcited by 2
  7. 2025Fast Rate Bounds for Multi-Task and Meta-Learning with Different Sample Sizescited by 2
  8. 2025Deploying Models to Non-participating Clients in Federated Learning without Fine-tuning: A Hypernetwork-based Approachcited by 2
  9. 2025Optimized Local Updates in Federated Learning via Reinforcement Learningcited by 1
  10. 2025Hypernetworks for Model-Heterogeneous Personalized Federated Learningcited by 1
  11. 2025Personalized Subgraph Federated Learning with Sheaf Collaborationcited by 1
  12. 2025Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Datacited by 0
9 more
  1. 2025Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensionscited by 0
  2. 2025Onboarding Without Forgetting: Hypernetwork Personalization with Data-Free Replay for Personalized Federated Learningcited by 0
  3. 2025Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participationcited by 0
  4. 2025AutoFed: Personalized Federated Traffic Prediction via Adaptive Promptcited by 0
  5. 2026Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learningcited by 0
  6. 2026Representation-Aligned Multi-Scale Personalization for Federated Learningcited by 0
  7. 2026FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEscited by 0
  8. 2026Similarity-Aware Personalized Federated Learning in Heterogeneous Environmentscited by 0
  9. 2026Privacy-Aligned Personalized Federated Learning with Compact Adaptation and Variable-Length Gaussian Communicationcited by 0