mlsanityData to 7 Oct 2026

FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy

arXiv 2310.09789 ↗First posted 15 Oct 2023cs.LGcited by 10

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Abstract (arXiv)

Federated Learning (FL) achieves great popularity in the Internet of Things (IoT) as a powerful interface to offer intelligent services to customers while maintaining data privacy. Under the orchestration of a server, edge devices (also called clients in FL) collaboratively train a global deep-learning model without sharing any local data. Nevertheless, the unequal training contributions among clients have made FL vulnerable, as clients with heavily biased datasets can easily compromise FL by sending malicious or heavily biased parameter updates. Furthermore, the resource shortage issue of the network also becomes a bottleneck. Due to overwhelming computation overheads generated by training deep-learning models on edge devices, and significant communication overheads for transmitting deep-learning models across the network, enormous amounts of resources are consumed in the FL process. This encompasses computation resources like energy and communication resources like bandwidth. To comprehensively address these challenges, in this paper, we present FLrce, an efficient FL framework with a relationship-based client selection and early-stopping strategy. FLrce accelerates the FL process by selecting clients with more significant effects, enabling the global model to converge to a high accuracy in fewer rounds. FLrce also leverages an early stopping mechanism that terminates FL in advance to save communication and computation resources. Experiment results show that, compared with existing efficient FL frameworks, FLrce improves the computation and communication efficiency by at least 30% and 43% respectively.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 34 of its 57 references that are in the map. First posted on arXiv 15 Oct 2023.

Builds on

34 of 57 references.

References · 34

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22 more
  1. 2021A Field Guide to Federated Optimizationcited by 312
  2. 2017TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learningcited by 253
  3. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
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  5. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
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  13. 2021Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devicescited by 56
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  18. 2023TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Trainingcited by 27
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 1.

10 papers.

  1. 2024Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classificationcited by 7
  2. 2024FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter Updatecited by 3
  3. 2025FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devicescited by 1
  4. 2025AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregatorcited by 1
  5. 2025Bridging Generalization Gap of Heterogeneous Federated Clients Using Generative Modelscited by 1
  6. 2025When to Stop Federated Learning: Zero-Shot Generation of Synthetic Validation Data with Generative AI for Early Stoppingcited by 1
  7. 2025Energy and Memory-Efficient Federated Learning With Ordered Layer Freezingcited by 1
  8. 2025FedSAF: A Federated Learning Framework for Enhanced Gastric Cancer Detection and Privacy Preservationcited by 0
  9. 2025FedFitTech: A Baseline in Federated Learning for Fitness Trackingcited by 0
  10. 2026Beyond Fixed Rounds: Data-Free Early Stopping for Practical Federated Learningcited by 0