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FedSkipTwin: Digital-Twin-Guided Client Skipping for Communication-Efficient Federated Learning

arXiv 2507.13624 ↗First posted 18 Jul 2025cs.LG cs.DC cs.NIcited by 0

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Abstract (arXiv)

Communication overhead remains a primary bottleneck in federated learning (FL), particularly for applications involving mobile and IoT devices with constrained bandwidth. This work introduces FedSkipTwin, a novel client-skipping algorithm driven by lightweight, server-side digital twins. Each twin, implemented as a simple LSTM, observes a client's historical sequence of gradient norms to forecast both the magnitude and the epistemic uncertainty of its next update. The server leverages these predictions, requesting communication only when either value exceeds a predefined threshold; otherwise, it instructs the client to skip the round, thereby saving bandwidth. Experiments are conducted on the UCI-HAR and MNIST datasets with 10 clients under a non-IID data distribution. The results demonstrate that FedSkipTwin reduces total communication by 12-15.5% across 20 rounds while simultaneously improving final model accuracy by up to 0.5 percentage points compared to the standard FedAvg algorithm. These findings establish that prediction-guided skipping is a practical and effective strategy for resource-aware FL in bandwidth-constrained edge environments.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 16 references that are in the map. First posted on arXiv 18 Jul 2025.

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References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 471
  4. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  5. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  6. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  7. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  8. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  9. 2020Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approachcited by 80
  10. 2025PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Frameworkcited by 4
  11. 2025ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofscited by 1
  12. 2025A Bayesian Incentive Mechanism for Poison-Resilient Federated Learningcited by 1

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