mlsanityData to 7 Oct 2026

Optimizing Federated Learning by Entropy-Based Client Selection

arXiv 2411.01240 ↗First posted 2 Nov 2024cs.LG cs.AI cs.DCcited by 3

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Abstract (arXiv)

Although deep learning has revolutionized domains such as natural language processing and computer vision, its dependence on centralized datasets raises serious privacy concerns. Federated learning addresses this issue by enabling multiple clients to collaboratively train a global deep learning model without compromising their data privacy. However, the performance of such a model degrades under label skew, where the label distribution differs between clients. To overcome this issue, a novel method called FedEntOpt is proposed. In each round, it selects clients to maximize the entropy of the aggregated label distribution, ensuring that the global model is exposed to data from all available classes. Extensive experiments on multiple benchmark datasets show that the proposed method outperforms several state-of-the-art algorithms by up to 6% in classification accuracy under standard settings regardless of the model size, while achieving gains of over 30% in scenarios with low participation rates and client dropout. In addition, FedEntOpt offers the flexibility to be combined with existing algorithms, enhancing their classification accuracy by more than 40%. Importantly, its performance remains unaffected even when differential privacy is applied.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 49 references that are in the map. First posted on arXiv 2 Nov 2024.

Builds on

21 of 49 references.

References · 21

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
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  6. 2019Towards Federated Learning at Scale: System Designcited by 826
  7. 2021Model-Contrastive Federated Learningcited by 762
  8. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 682
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  12. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
9 more
  1. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 108
  2. 2020Fast-Convergent Federated Learning with Adaptive Weightingcited by 67
  3. 2021Deep Federated Learning for Autonomous Drivingcited by 54
  4. 2021FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learningcited by 43
  5. 2023Exploiting Label Skews in Federated Learning with Model Concatenationcited by 14
  6. 2023Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Datacited by 7
  7. 2023A Privacy Preserving System for Movie Recommendations Using Federated Learningcited by 6
  8. 2022FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgmentcited by 3
  9. 2023Adaptive Federated Learning via New Entropy Approachcited by 3

Built on by

Cited by year (2026 to 7 Oct)

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

3 papers.

  1. 2025FedPPA: Progressive Parameter Alignment for Personalized Federated Learningcited by 0
  2. 2026FedPrism: Adaptive Personalized Federated Learning under Non-IID Datacited by 0
  3. 2026Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalizationcited by 0