mlsanityData to 14 Oct 2026

FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment

arXiv 2305.06124 ↗First posted 10 May 2023cs.LGcited by 9

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Abstract (arXiv)

Different from conventional federated learning, personalized federated learning (PFL) is able to train a customized model for each individual client according to its unique requirement. The mainstream approach is to adopt a kind of weighted aggregation method to generate personalized models, in which weights are determined by the loss value or model parameters among different clients. However, such kinds of methods require clients to download others' models. It not only sheer increases communication traffic but also potentially infringes data privacy. In this paper, we propose a new PFL algorithm called FedDWA (Federated Learning with Dynamic Weight Adjustment) to address the above problem, which leverages the parameter server (PS) to compute personalized aggregation weights based on collected models from clients. In this way, FedDWA can capture similarities between clients with much less communication overhead. More specifically, we formulate the PFL problem as an optimization problem by minimizing the distance between personalized models and guidance models, so as to customize aggregation weights for each client. Guidance models are obtained by the local one-step ahead adaptation on individual clients. Finally, we conduct extensive experiments using five real datasets and the results demonstrate that FedDWA can significantly reduce the communication traffic and achieve much higher model accuracy than the state-of-the-art approaches.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 37 of its 51 references that are in the map. First posted on arXiv 10 May 2023.

Builds on

37 of 51 references.

References · 37

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  4. 2018Federated Learning with Non-IID Datacited by 1,108
  5. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  6. 2021Model-Contrastive Federated Learningcited by 766
  7. 2017Federated Multi-Task Learningcited by 672
  8. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  9. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  10. 2019Federated Learning with Personalization Layerscited by 541
  11. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  12. 2020An Efficient Framework for Clustered Federated Learningcited by 511
25 more
  1. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  2. 2017A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasetscited by 377
  3. 2020Adaptive Personalized Federated Learningcited by 358
  4. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 352
  5. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  6. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  7. 2021Personalized Federated Learning using Hypernetworkscited by 212
  8. 2020Federated Learning of a Mixture of Global and Local Modelscited by 210
  9. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  10. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  11. 2019Adaptive Gradient-Based Meta-Learning Methodscited by 183
  12. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 182
  13. 2019Federated Evaluation of On-device Personalizationcited by 147
  14. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  15. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 109
  16. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  17. 2021Personalized Federated Learning with Gaussian Processescited by 76
  18. 2020XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learningcited by 58
  19. 2021Personalized Federated Learning through Local Memorizationcited by 57
  20. 2020Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computingcited by 52
  21. 2021Source Inference Attacks in Federated Learningcited by 34
  22. 2022Personalized Federated Learning With Graphcited by 29
  23. 2020Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learningcited by 22
  24. 2014Diffusion LMS over Multitask Networkscited by 5
  25. 2020Affine Combination of Diffusion Strategies over Networkscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 1.

9 papers.

  1. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  2. 2024Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learningcited by 5
  3. 2025FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learningcited by 4
  4. 2025pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixupcited by 3
  5. 2025Adaptive collaboration for online personalized distributed learning with heterogeneous clientscited by 2
  6. 2025Operator-Theoretic Framework for Gradient-Free Federated Learningcited by 2
  7. 2025pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimizationcited by 1
  8. 2025FedDAF: Federated Domain Adaptation Using Model Functional Distancecited by 0
  9. 2026Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learningcited by 0