{"ID":2892943,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.13624","arxiv_id":"2507.13624","title":"FedSkipTwin: Digital-Twin-Guided Client Skipping for Communication-Efficient Federated Learning","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.","short_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 sim...","url_abs":"https://arxiv.org/abs/2507.13624","url_pdf":"https://arxiv.org/pdf/2507.13624v1","authors":"[\"Daniel Commey\",\"Kamel Abbad\",\"Garth V. Crosby\",\"Lyes Khoukhi\"]","published":"2025-07-18T03:39:08Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.DC\",\"cs.NI\"]","methods":"[]","has_code":false}