标题： FedSkipTwin：数字孪生引导的客户端跳过以实现通信高效的联邦学习 显示英文标题

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

摘要： 通信开销仍然是联邦学习(FL)中的主要瓶颈，尤其是在移动和物联网设备应用中，这些设备带宽受限。这项工作引入了FedSkipTwin，这是一种由轻量级服务器端数字孪生驱动的新型客户端跳过算法。每个孪生体被实现为一个简单的LSTM，观察客户端的历史梯度范数序列，以预测其下一次更新的幅度和认识不确定性。当任一值超过预定义阈值时，服务器利用这些预测请求通信；否则，它会指示客户端跳过该轮次，从而节省带宽。在非独立同分布数据分布下，对UCI-HAR和MNIST数据集上的10个客户端进行了实验。结果表明，与标准的FedAvg算法相比，FedSkipTwin在20轮中总通信量减少了12-15.5%，同时最终模型精度提高了多达0.5个百分点。这些发现表明，基于预测的跳过是一种在带宽受限边缘环境中资源感知联邦学习的实用且有效策略。 显示英文摘要

摘要： 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.