mlsanityData to 7 Oct 2026

Self-Aware Personalized Federated Learning

arXiv 2204.08069 ↗First posted 17 Apr 2022cs.LG cs.AIcited by 16

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Abstract (arXiv)

In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives may not be exactly aligned. Inspired by Bayesian hierarchical models, we develop a self-aware personalized FL method where each client can automatically balance the training of its local personal model and the global model that implicitly contributes to other clients' training. Such a balance is derived from the inter-client and intra-client uncertainty quantification. A larger inter-client variation implies more personalization is needed. Correspondingly, our method uses uncertainty-driven local training steps and aggregation rule instead of conventional local fine-tuning and sample size-based aggregation. With experimental studies on synthetic data, Amazon Alexa audio data, and public datasets such as MNIST, FEMNIST, CIFAR10, and Sent140, we show that our proposed method can achieve significantly improved personalization performance compared with the existing counterparts.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 34 of its 51 references that are in the map. First posted on arXiv 17 Apr 2022.

Builds on

34 of 51 references.

References · 34

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2020Adaptive Federated Optimizationcited by 1,036
  6. 2019Towards Federated Learning at Scale: System Designcited by 826
  7. 2017Federated Multi-Task Learningcited by 669
  8. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  9. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  10. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  11. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  12. 2019Fair Resource Allocation in Federated Learningcited by 389
22 more
  1. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  2. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  3. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  4. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  5. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  6. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  7. 2021Personalized Federated Learning using Hypernetworkscited by 210
  8. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  9. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  10. 2019Adaptive Gradient-Based Meta-Learning Methodscited by 183
  11. 2020FedBE: Making Bayesian Model Ensemble Applicable to Federated Learningcited by 176
  12. 2016QSGD: Communication-Efficient SGD via Gradient Quantization and Encodingcited by 147
  13. 2019Federated Evaluation of On-device Personalizationcited by 147
  14. 2020Lower Bounds and Optimal Algorithms for Personalized Federated Learningcited by 122
  15. 2019Communication-efficient distributed SGD with Sketchingcited by 81
  16. 2020Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithmscited by 78
  17. 2016Decentralized Collaborative Learning of Personalized Models over Networkscited by 73
  18. 2020LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasetscited by 60
  19. 2021Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharingcited by 58
  20. 2022Federated Learning Challenges and Opportunities: An Outlookcited by 19
  21. 2021On Data Efficiency of Meta-learningcited by 16
  22. 2020Information Laundering for Model Privacycited by 13

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Citing papers per year: 2022: 1; 2023: 4; 2024: 7; 2025: 4.

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  1. 2023Personalized Federated Learning under Mixture of Distributionscited by 37
  2. 2023Bayesian Federated Learning: A Surveycited by 22
  3. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
  4. 2024Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuningcited by 8
  5. 2025Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Associationcited by 7
  6. 2022FedRN: Exploiting k-Reliable Neighbors Towards Robust Federated Learningcited by 2
  7. 2023A Framework for Incentivized Collaborative Learningcited by 2
  8. 2024Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Reviewcited by 2
  9. 2024ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learningcited by 1
  10. 2024DynamicFL: Federated Learning with Dynamic Communication Resource Allocationcited by 1
  11. 2024Adaptive Client Selection with Personalization for Communication Efficient Federated Learningcited by 1
  12. 2025Achieving Distributive Justice in Federated Learning via Uncertainty Quantificationcited by 1
4 more
  1. 2023IP-FL: Incentivized and Personalized Federated Learningcited by 0
  2. 2024Emergence of Self-Identity in AI: A Mathematical Framework and Empirical Study with Generative Large Language Modelscited by 0
  3. 2025SPIRE: Conditional Personalization for Federated Diffusion Generative Modelscited by 0
  4. 2025Iterative Foundation Model Fine-Tuning on Multiple Rewardscited by 0