mlsanityData to 7 Oct 2026

Confidence-aware Personalized Federated Learning via Variational Expectation Maximization

arXiv 2305.12557 ↗First posted 21 May 2023cs.LG cs.AIcited by 17

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Abstract (arXiv)

Federated Learning (FL) is a distributed learning scheme to train a shared model across clients. One common and fundamental challenge in FL is that the sets of data across clients could be non-identically distributed and have different sizes. Personalized Federated Learning (PFL) attempts to solve this challenge via locally adapted models. In this work, we present a novel framework for PFL based on hierarchical Bayesian modeling and variational inference. A global model is introduced as a latent variable to augment the joint distribution of clients' parameters and capture the common trends of different clients, optimization is derived based on the principle of maximizing the marginal likelihood and conducted using variational expectation maximization. Our algorithm gives rise to a closed-form estimation of a confidence value which comprises the uncertainty of clients' parameters and local model deviations from the global model. The confidence value is used to weigh clients' parameters in the aggregation stage and adjust the regularization effect of the global model. We evaluate our method through extensive empirical studies on multiple datasets. Experimental results show that our approach obtains competitive results under mild heterogeneous circumstances while significantly outperforming state-of-the-art PFL frameworks in highly heterogeneous settings. Our code is available at https://github.com/JunyiZhu-AI/confidence_aware_PFL.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 30 of its 45 references that are in the map. First posted on arXiv 21 May 2023.

Builds on

30 of 45 references.

References · 30

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  7. 2020Adaptive Federated Optimizationcited by 1,036
  8. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  9. 2021Model-Contrastive Federated Learningcited by 762
  10. 2017Federated Multi-Task Learningcited by 669
  11. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  12. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
18 more
  1. 2019Federated Learning with Personalization Layerscited by 533
  2. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  3. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  4. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  5. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  6. 2021Personalized Federated Learning using Hypernetworkscited by 210
  7. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  8. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  9. 2020FedBE: Making Bayesian Model Ensemble Applicable to Federated Learningcited by 176
  10. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 108
  11. 2020Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithmscited by 78
  12. 2021Personalized Federated Learning with Gaussian Processescited by 76
  13. 2019Variational Federated Multi-Task Learningcited by 69
  14. 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
  15. 2021Personalized Federated Learning through Local Memorizationcited by 57
  16. 2018Effects of Degradations on Deep Neural Network Architecturescited by 47
  17. 2021An Expectation-Maximization Perspective on Federated Learningcited by 9
  18. 2022Personalized Federated Learning with Exact Stochastic Gradient Descentcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 10; 2025: 3; 2026: 2.

17 papers.

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  1. 2024Implicit Neural Representations for Robust Joint Sparse-View CT Reconstructioncited by 9
  2. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  3. 2024Balancing Similarity and Complementarity for Federated Learningcited by 7
  4. 2023FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learningcited by 5
  5. 2024Prompt-based Personalized Federated Learning for Medical Visual Question Answeringcited by 4
  6. 2023FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentationcited by 3
  7. 2024Variational Bayes for Federated Continual Learningcited by 1
  8. 2024Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimizationcited by 1
  9. 2024Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regressioncited by 1
  10. 2024Information-Geometric Barycenters for Bayesian Federated Learningcited by 1
  11. 2025Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregationcited by 1
  12. 2024FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentationcited by 0
5 more
  1. 2024Your Data, My Model: Learning Who Really Helps in Federated Learningcited by 0
  2. 2025Guided Model Merging for Hybrid Data Learning: Leveraging Centralized Data to Refine Decentralized Modelscited by 0
  3. 2025Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learningcited by 0
  4. 2026Federated Learning at the Forefront of Fairness: A Multifaceted Perspectivecited by 0
  5. 2026HiLoRA: Hierarchical Low-Rank Adaptation for Personalized Federated Learningcited by 0