mlsanityData to 14 Oct 2026

DPP-based Client Selection for Federated Learning with Non-IID Data

arXiv 2303.17358 ↗First posted 30 Mar 2023cs.LGcited by 3

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Abstract (arXiv)

This paper proposes a client selection (CS) method to tackle the communication bottleneck of federated learning (FL) while concurrently coping with FL's data heterogeneity issue. Specifically, we first analyze the effect of CS in FL and show that FL training can be accelerated by adequately choosing participants to diversify the training dataset in each round of training. Based on this, we leverage data profiling and determinantal point process (DPP) sampling techniques to develop an algorithm termed Federated Learning with DPP-based Participant Selection (FL-DP3S). This algorithm effectively diversifies the participants' datasets in each round of training while preserving their data privacy. We conduct extensive experiments to examine the efficacy of our proposed method. The results show that our scheme attains a faster convergence rate, as well as a smaller communication overhead than several baselines.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 34 references that are in the map. First posted on arXiv 30 Mar 2023.

Builds on

18 of 34 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2018Federated Learning with Non-IID Datacited by 1,108
  6. 2019Towards Federated Learning at Scale: System Designcited by 832
  7. 2019Federated Machine Learning: Concept and Applicationscited by 412
  8. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  9. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 305
  10. 2018Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Datacited by 243
  11. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  12. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
6 more
  1. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
  2. 2019Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applicationscited by 64
  3. 2019Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Datacited by 38
  4. 2014Accelerating Minibatch Stochastic Gradient Descent using Stratified Samplingcited by 21
  5. 2018Active Mini-Batch Sampling using Repulsive Point Processescited by 20
  6. 2021FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systemscited by 16

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Cited by year (2026 to 14 Oct)

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

3 papers.

  1. 2026SPICE: Submodular Penalized Information-Conflict Selection for Efficient Large Language Model Trainingcited by 7
  2. 2025Long-Term Client Selection for Federated Learning with Non-IID Data: A Truthful Auction Approachcited by 0
  3. 2026Adaptive Determinantal Client Scheduling in Federated Learningcited by 0