mlsanityData to 7 Oct 2026

Personalized Federated Learning with Server-Side Information

arXiv 2205.11044 ↗First posted 23 May 2022cs.LG cs.AIcited by 6

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Abstract (arXiv)

Personalized Federated Learning (FL) is an emerging research field in FL that learns an easily adaptable global model in the presence of data heterogeneity among clients. However, one of the main challenges for personalized FL is the heavy reliance on clients' computing resources to calculate higher-order gradients since client data is segregated from the server to ensure privacy. To resolve this, we focus on a problem setting where the server may possess its own data independent of clients' data -- a prevalent problem setting in various applications, yet relatively unexplored in existing literature. Specifically, we propose FedSIM, a new method for personalized FL that actively utilizes such server data to improve meta-gradient calculation in the server for increased personalization performance. Experimentally, we demonstrate through various benchmarks and ablations that FedSIM is superior to existing methods in terms of accuracy, more computationally efficient by calculating the full meta-gradients in the server, and converges up to 34.2% faster.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 34 of its 44 references that are in the map. First posted on arXiv 23 May 2022.

Builds on

34 of 44 references.

References · 34

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  6. 2018Federated Learning with Non-IID Datacited by 1,099
  7. 2020Adaptive Federated Optimizationcited by 1,036
  8. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  9. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  10. 2019Towards Federated Learning at Scale: System Designcited by 826
  11. 2018LEAF: A Benchmark for Federated Settingscited by 780
  12. 2020Personalized Federated Learning with Moreau Envelopescited by 612
22 more
  1. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  2. 2019Federated Learning with Personalization Layerscited by 533
  3. 2019Meta-Learning with Implicit Gradientscited by 523
  4. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  5. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  6. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  7. 2019Tighter Theory for Local SGD on Identical and Heterogeneous Datacited by 266
  8. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  9. 2017EMNIST: an extension of MNIST to handwritten letterscited by 212
  10. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  11. 2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 154
  12. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 150
  13. 2020Minibatch vs Local SGD for Heterogeneous Distributed Learningcited by 147
  14. 2019On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithmscited by 132
  15. 2020Lower Bounds and Optimal Algorithms for Personalized Federated Learningcited by 122
  16. 2020FedSplit: An algorithmic framework for fast federated optimizationcited by 108
  17. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 108
  18. 2019Communication-efficient distributed SGD with Sketchingcited by 81
  19. 2021Personalized Federated Learning with Gaussian Processescited by 76
  20. 2020Federated Learning With Quantized Global Model Updatescited by 54
  21. 2019Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradientscited by 32
  22. 2022PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networkscited by 14

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 3; 2025: 1.

6 papers.

  1. 2023Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Modelscited by 10
  2. 2023Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labelingcited by 4
  3. 2024Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomialscited by 3
  4. 2024Towards Fairness in Provably Communication-Efficient Federated Recommender Systemscited by 0
  5. 2024UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Taskscited by 0
  6. 2025Guided Model Merging for Hybrid Data Learning: Leveraging Centralized Data to Refine Decentralized Modelscited by 0