mlsanityData to 7 Oct 2026

Federated Learning with Server Learning: Enhancing Performance for Non-IID Data

arXiv 2210.02614 ↗First posted 6 Oct 2022cs.LG cs.DC math.OCcited by 6

Not on the map. Why?

Abstract (arXiv)

Federated Learning (FL) has emerged as a means of distributed learning using local data stored at clients with a coordinating server. Recent studies showed that FL can suffer from poor performance and slower convergence when training data at clients are not independent and identically distributed. Here we consider a new complementary approach to mitigating this performance degradation by allowing the server to perform auxiliary learning from a small dataset. Our analysis and experiments show that this new approach can achieve significant improvements in both model accuracy and convergence time even when the server dataset is small and its distribution differs from that of the aggregated data from all clients.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 6 Oct 2022.

Builds on

24 of 35 references.

References · 24

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2018Federated Learning with Non-IID Datacited by 1,099
  6. 2020Adaptive Federated Optimizationcited by 1,036
  7. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  8. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  9. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  10. 2020Adaptive Personalized Federated Learningcited by 353
  11. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  12. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
12 more
  1. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  2. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 150
  3. 2020Survey of Personalization Techniques for Federated Learningcited by 144
  4. 2019Overcoming Forgetting in Federated Learning on Non-IID Datacited by 120
  5. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  6. 2020From Local SGD to Local Fixed-Point Methods for Federated Learningcited by 74
  7. 2020Fast-Convergent Federated Learning with Adaptive Weightingcited by 67
  8. 2015Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Surveycited by 44
  9. 2020Inverse Distance Aggregation for Federated Learning with Non-IID Datacited by 44
  10. 2019Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Datacited by 38
  11. 2021Federated Learning with Taskonomy for Non-IID Datacited by 21
  12. 2022Mixed Federated Learning: Joint Decentralized and Centralized Learningcited by 8

Built on by

Cited by year

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

6 papers.

  1. 2023Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networkscited by 8
  2. 2024Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomialscited by 3
  3. 2024FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantizationcited by 1
  4. 2025The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directionscited by 1
  5. 2024UniFed: A Universal Federation of a Mixture of Highly Heterogeneous Medical Image Classification Taskscited by 0
  6. 2025Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domainscited by 0