mlsanityData to 14 Oct 2026

Personalized Federated Learning with Attention-based Client Selection

arXiv 2312.15148 ↗First posted 23 Dec 2023cs.LG cs.IT eess.SPcited by 5

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Abstract (arXiv)

Personalized Federated Learning (PFL) relies on collective data knowledge to build customized models. However, non-IID data between clients poses significant challenges, as collaborating with clients who have diverse data distributions can harm local model performance, especially with limited training data. To address this issue, we propose FedACS, a new PFL algorithm with an Attention-based Client Selection mechanism. FedACS integrates an attention mechanism to enhance collaboration among clients with similar data distributions and mitigate the data scarcity issue. It prioritizes and allocates resources based on data similarity. We further establish the theoretical convergence behavior of FedACS. Experiments on CIFAR10 and FMNIST validate FedACS's superiority, showcasing its potential to advance personalized federated learning. By tackling non-IID data challenges and data scarcity, FedACS offers promising advances in the field of personalized federated learning.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 23 Dec 2023.

Builds on

17 of 26 references.

References · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  6. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  7. 2018Federated Learning with Non-IID Datacited by 1,108
  8. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  9. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  10. 2020Adaptive Personalized Federated Learningcited by 358
  11. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 334
  12. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
5 more
  1. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  2. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 269
  3. 2020Survey of Personalization Techniques for Federated Learningcited by 145
  4. 2020FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Datacited by 144
  5. 2015Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Surveycited by 45

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learningcited by 3
  2. 2026Personalized Digital Health Modeling with Adaptive Support Userscited by 0
  3. 2026Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attentioncited by 0
  4. 2026Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learningcited by 0
  5. 2026Similarity-Aware Personalized Federated Learning in Heterogeneous Environmentscited by 0