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arXiv · 2507.13624

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

Daniel Commey·Kamel Abbad·Garth V. Crosby·Lyes Khoukhi
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Abstract

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.

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Daniel Commey, Kamel Abbad, Garth V. Crosby, Lyes Khoukhi. 2025-07-18. FedSkipTwin: Digital-Twin-Guided Client Skipping for Communication-Efficient Federated Learning. https://doi.org/10.1109/ccnc65079.2026.11366293

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