mlsanityData to 7 Oct 2026

FedSS: Federated Learning with Smart Selection of clients

arXiv 2207.04569 ↗First posted 10 Jul 2022cs.DC cs.LG cs.NIcited by 4

Not on the map. Why?

Abstract (arXiv)

Federated learning provides the ability to learn over heterogeneous user data in a distributed manner while preserving user privacy. However, its current client selection technique is a source of bias as it discriminates against slow clients. For starters, it selects clients that satisfy certain network and system-specific criteria, thus not selecting slow clients. Even when such clients are included in the training process, they either struggle with the training or are dropped altogether for being too slow. Our proposed idea looks to find a sweet spot between fast convergence and heterogeneity by looking at smart client selection and scheduling techniques.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 21 of its 34 references that are in the map. First posted on arXiv 10 Jul 2022.

Builds on

21 of 34 references.

References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  7. 2018Federated Learning with Non-IID Datacited by 1,099
  8. 2019Towards Federated Learning at Scale: System Designcited by 826
  9. 2018LEAF: A Benchmark for Federated Settingscited by 780
  10. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  11. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  12. 2019Asynchronous Federated Optimizationcited by 264
9 more
  1. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 242
  2. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  3. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  4. 2020TiFL: A Tier-based Federated Learning Systemcited by 95
  5. 2020Towards Flexible Device Participation in Federated Learningcited by 64
  6. 2020Mitigating Bias in Federated Learningcited by 56
  7. 2018Toward Understanding the Impact of Staleness in Distributed Machine Learningcited by 28
  8. 2019Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaborationcited by 15
  9. 2021Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settingscited by 11

Built on by

Cited by year (2026 to 7 Oct)

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

4 papers.

  1. 2025A Two-Stage Data Selection Framework for Data-Efficient Model Training on Edge Devicescited by 2
  2. 2024Seamless Integration: Sampling Strategies in Federated Learning Systemscited by 1
  3. 2025FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairnesscited by 0
  4. 2026ProToken: Token-Level Attribution for Federated Large Language Modelscited by 0