用数字孪生预测客户端更新价值，减少通信量同时提升精度
FedSkipTwin: Digital-Twin-Guided Client Skipping for Communication-Efficient Federated Learning
- 用轻量LSTM构建客户端数字孪生，预测梯度大小与不确定性
- 通信量减少12%-15.5%，最终准确率提升最高0.5个百分点
- 适合带宽受限的移动与物联网设备联邦学习场景
通信开销仍是联邦学习（FL）的主要瓶颈，尤其在移动和物联网设备等带宽受限的场景中。本文提出FedSkipTwin，一种基于轻量级服务器端数字孪生的客户端跳过算法。每个数字孪生以简单LSTM模型观察客户端历史梯度范数序列，预测其下一次更新的幅度及认知不确定性。当预测值超过预设阈值时，服务器才请求通信；否则指示客户端跳过本轮，节省带宽。在非独立同分布（non-IID）数据下，于UCI-HAR与MNIST数据集上，10个客户端运行20轮实验表明，该方法在20轮内总通信量降低12%-15.5%，同时最终模型准确率相较标准FedAvg提升最多0.5个百分点。结果表明，基于预测的跳过策略是资源敏感型边缘联邦学习中切实有效的方案。
原文摘要 · Abstract (English)
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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