Federated edge learning (FEEL) is a popular distributed learning framework for privacy-preserving at the edge, in which densely distributed edge devices periodically exchange model-updates with the server to complete the global model training. Due to limited bandwidth and uncertain wireless environment, FEEL may impose heavy burden to the current communication system. In addition, under the common FEEL framework, the server needs to wait for the slowest device to complete the update uploading before starting the aggregation process, leading to the straggler issue that causes prolonged communication time. In this paper, we propose to accelerate FEEL from two aspects: i.e., 1) performing data compression on the edge devices and 2) setting a deadline on the edge server to exclude the straggler devices. However, undesired gradient compression errors and transmission outage are introduced by the aforementioned operations respectively, affecting the convergence of FEEL as well. In view of these practical issues, we formulate a training time minimization problem, with the compression ratio and deadline to be optimized. To this end, an asymptotically unbiased aggregation scheme is first proposed to ensure zero optimality gap after convergence, and the impact of compression error and transmission outage on the overall training time are quantified through convergence analysis. Then, the formulated problem is solved in an alternating manner, based on which, the novel joint compression and deadline optimization (JCDO) algorithm is derived. Numerical experiments for different use cases in FEEL including image classification and autonomous driving show that the proposed method is nearly 30X faster than the vanilla FedAVG algorithm, and outperforms the state-of-the-art schemes.

翻译：联邦边缘学习（FEEL）是一种适用于边缘隐私保护的流行分布式学习框架，其大量分布式边缘设备定期与服务器交换模型更新以完成全局模型训练。由于有限带宽和不确定的无线环境，FEEL可能给当前通信系统带来沉重负担。此外，在常规FEEL框架下，服务器需等待最慢设备完成更新上传后才能开始聚合过程，这引发拖累问题并导致通信时间延长。本文提出从两方面加速FEEL：1）在边缘设备上执行数据压缩，2）在边缘服务器设置截止时间以排除拖累设备。然而，上述操作分别引入了梯度压缩误差和传输中断问题，同样影响FEEL收敛性。针对这些实际问题，我们构建了一个训练时间最小化问题，需要优化压缩率和截止时间。为此，本文首先提出一种渐进无偏的聚合方案，确保收敛后最优性差距为零，并通过收敛性分析量化压缩误差和传输中断对整体训练时间的影响。随后，采用交替优化方式求解该问题，并据此推导出新颖的联合压缩与截止时间优化（JCDO）算法。在包含图像分类和自动驾驶等不同FEEL应用场景的数值实验中，该方法比原始FedAVG算法加速近30倍，且性能优于现有最优方案。