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FedL2P: Federated Learning to Personalize

arXiv 2310.02420 ↗First posted 3 Oct 2023cs.LG cs.CV cs.DCcited by 0

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Abstract (arXiv)

Federated learning (FL) research has made progress in developing algorithms for distributed learning of global models, as well as algorithms for local personalization of those common models to the specifics of each client's local data distribution. However, different FL problems may require different personalization strategies, and it may not even be possible to define an effective one-size-fits-all personalization strategy for all clients: depending on how similar each client's optimal predictor is to that of the global model, different personalization strategies may be preferred. In this paper, we consider the federated meta-learning problem of learning personalization strategies. Specifically, we consider meta-nets that induce the batch-norm and learning rate parameters for each client given local data statistics. By learning these meta-nets through FL, we allow the whole FL network to collaborate in learning a customized personalization strategy for each client. Empirical results show that this framework improves on a range of standard hand-crafted personalization baselines in both label and feature shift situations.

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References · 50

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  4. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  5. 2019Advances and Open Problems in Federated Learningcited by 3,021
  6. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  7. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  8. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,031
  9. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,327
  10. 2018Federated Learning with Non-IID Datacited by 1,099
  11. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  12. 2021Model-Contrastive Federated Learningcited by 762
38 more
  1. 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 741
  2. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  3. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  4. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  5. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  6. 2019Federated Learning with Personalization Layerscited by 533
  7. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  8. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  9. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  10. 2018TADAM: Task dependent adaptive metric for improved few-shot learningcited by 408
  11. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  12. 2020Adaptive Personalized Federated Learningcited by 353
  13. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  14. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  15. 2019Optimizing Millions of Hyperparameters by Implicit Differentiationcited by 303
  16. 2016Revisiting Batch Normalization For Practical Domain Adaptationcited by 285
  17. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  18. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  19. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  20. 2021Personalized Federated Learning using Hypernetworkscited by 210
  21. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  22. 2019Domain-Specific Batch Normalization for Unsupervised Domain Adaptationcited by 197
  23. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  24. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  25. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 180
  26. 2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 154
  27. 2022Layer-wised Model Aggregation for Personalized Federated Learningcited by 95
  28. 2020Meta-Learning with Adaptive Hyperparameterscited by 79
  29. 2020Federated Mutual Learningcited by 70
  30. 2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
  31. 2020Auxiliary Learning by Implicit Differentiationcited by 45
  32. 2022ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsitycited by 45
  33. 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
  34. 2021Meta-learning with negative learning ratescited by 11
  35. 2021FedMe: Federated Learning via Model Exchangecited by 10
  36. 2021Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning Systemcited by 8
  37. 2022FedDAR: Federated Domain-Aware Representation Learningcited by 8
  38. 2022An Empirical Study of Personalized Federated Learningcited by 6

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