mlsanityData to 14 Oct 2026

FedPop: A Bayesian Approach for Personalised Federated Learning

arXiv 2206.03611 ↗First posted 7 Jun 2022cs.LG stat.ME stat.MLcited by 35

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Abstract (arXiv)

Personalised federated learning (FL) aims at collaboratively learning a machine learning model taylored for each client. Albeit promising advances have been made in this direction, most of existing approaches works do not allow for uncertainty quantification which is crucial in many applications. In addition, personalisation in the cross-device setting still involves important issues, especially for new clients or those having small number of observations. This paper aims at filling these gaps. To this end, we propose a novel methodology coined FedPop by recasting personalised FL into the population modeling paradigm where clients' models involve fixed common population parameters and random effects, aiming at explaining data heterogeneity. To derive convergence guarantees for our scheme, we introduce a new class of federated stochastic optimisation algorithms which relies on Markov chain Monte Carlo methods. Compared to existing personalised FL methods, the proposed methodology has important benefits: it is robust to client drift, practical for inference on new clients, and above all, enables uncertainty quantification under mild computational and memory overheads. We provide non-asymptotic convergence guarantees for the proposed algorithms and illustrate their performances on various personalised federated learning tasks.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). Builds on 35 of its 74 references that are in the map. First posted on arXiv 7 Jun 2022.

Builds on

35 of 74 references.

References · 35

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2017Federated Multi-Task Learningcited by 672
  5. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  6. 2019Federated Learning with Personalization Layerscited by 541
  7. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  8. 2021Towards Personalized Federated Learningcited by 464
  9. 2020Adaptive Personalized Federated Learningcited by 358
  10. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
  11. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  12. 2021A Field Guide to Federated Optimizationcited by 315
23 more
  1. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 269
  2. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 223
  3. 2020Federated Learning of a Mixture of Global and Local Modelscited by 210
  4. 2014Theoretical guarantees for approximate sampling from smooth and log-concave densitiescited by 208
  5. 2015Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithmcited by 197
  6. 2018Bayesian Model-Agnostic Meta-Learningcited by 194
  7. 2019Adaptive Gradient-Based Meta-Learning Methodscited by 183
  8. 2018Recasting Gradient-Based Meta-Learning as Hierarchical Bayescited by 180
  9. 2020FedBE: Making Bayesian Model Ensemble Applicable to Federated Learningcited by 176
  10. 2020Lower Bounds and Optimal Algorithms for Personalized Federated Learningcited by 123
  11. 2022Federated Learning with Partial Model Personalizationcited by 114
  12. 2021Federated Reconstruction: Partially Local Federated Learningcited by 79
  13. 2019Variational Federated Multi-Task Learningcited by 69
  14. 2019Stochastic Distributed Learning with Gradient Quantization and Variance Reductioncited by 65
  15. 2020Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guaranteescited by 46
  16. 2021Hierarchical Bayesian Banditscited by 35
  17. 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniquescited by 34
  18. 2018Partitioned Variational Inference: A unified framework encompassing federated and continual learningcited by 29
  19. 2021QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learningcited by 26
  20. 2021On Convergence of Federated Averaging Langevin Dynamicscited by 14
  21. 2021DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbscited by 11
  22. 2021Wireless Federated Langevin Monte Carlo: Repurposing Channel Noise for Bayesian Sampling and Privacycited by 10
  23. 2021Channel-Driven Monte Carlo Sampling for Bayesian Distributed Learning in Wireless Data Centerscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 12; 2024: 12; 2025: 10; 2026: 1.

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  1. 2023Personalized Federated Learning under Mixture of Distributionscited by 37
  2. 2024From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimationcited by 28
  3. 2023One-Shot Federated Conformal Predictioncited by 27
  4. 2023Bayesian Federated Learning: A Surveycited by 22
  5. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
  6. 2023FedHB: Hierarchical Bayesian Federated Learningcited by 8
  7. 2023Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networkscited by 8
  8. 2023Federated Variational Inference Methods for Structured Latent Variable Modelscited by 4
  9. 2024FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantificationcited by 4
  10. 2024Marginal and training-conditional guarantees in one-shot federated conformal predictioncited by 3
  11. 2024Federated Automatic Latent Variable Selection in Multi-output Gaussian Processescited by 3
  12. 2025Federated ADMM from Bayesian Dualitycited by 3
23 more
  1. 2023Federated Variational Inference: Towards Improved Personalization and Generalizationcited by 2
  2. 2023Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representationscited by 2
  3. 2023Federated Learning with Nonvacuous Generalisation Boundscited by 2
  4. 2024Scalable Vertical Federated Learning via Data Augmentation and Amortized Inferencecited by 2
  5. 2024Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Reviewcited by 2
  6. 2023Personalized Federated Learning via Amortized Bayesian Meta-Learningcited by 1
  7. 2023A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasetscited by 1
  8. 2023PPFL: A Personalized Federated Learning Framework for Heterogeneous Populationcited by 1
  9. 2024ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learningcited by 1
  10. 2024Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tunecited by 1
  11. 2024Information-Geometric Barycenters for Bayesian Federated Learningcited by 1
  12. 2025Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Frameworkcited by 1
  13. 2025Towards Instance-wise Personalized Federated Learning via Semi-Implicit Bayesian Prompt Tuningcited by 1
  14. 2025Who to Trust? Aggregating Client Predictions in Federated Distillationcited by 1
  15. 2025Federated Learning via Meta-Variational Dropoutcited by 1
  16. 2024Federated Learning for Non-factorizable Models using Deep Generative Prior Approximationscited by 0
  17. 2024Personalizing Low-Rank Bayesian Neural Networks Via Federated Learningcited by 0
  18. 2025Bayesian Federated Cause-of-Death Classification and Quantification Under Distribution Shiftcited by 0
  19. 2025Adaptive Latent-Space Constraints in Personalized Federated Learningcited by 0
  20. 2025SPIRE: Conditional Personalization for Federated Diffusion Generative Modelscited by 0
  21. 2025Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learningcited by 0
  22. 2025Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalizationcited by 0
  23. 2026Uncertainty Quantification in Federated Granger Causality Learningcited by 0