Prototype-Based Semi-Asynchronous Edge-End Collaborative Learning with Client Clustering

Edge-end collaborative learning greatly reduces latency by eliminating the need for processing on the cloud side, showing promising results in machine learning applications due to collaborating on training tasks through the computational resources of edge servers and end devices. However, current edge-end collaborative learning methods suffer from unbearable latency which result from heavy transmission burden and long synchronization time. We propose a new Prototype-based Semi-Asynchronous Edge-end Collaborative Learning approach (ProSACL) that carefully integrates prototype-based end-side training and edge clustering, allowing any end device to synchronize knowledge, significantly reducing the time spent on training latency. Our approach includes (1) prototype training and asynchronous prototype transfer on end devices: Unlike traditional training methods, we use the average of the same class of feature vectors, i.e., the prototype, as the knowledge transfer means, which significantly reduces the transfer burden and eliminates the need for end devices to wait for other devices to finish. (2) prototype-based clustering and asynchronous aggregation on the edge server: The end devices are segmented by prototype-based clustering to obtain unbiased prototypes for performance enhancement, and prototypes from different rounds are aggregated for fine-grained knowledge transfer. We evaluate the proposed approach by training on three datasets, which show substantial performance improvement compared to previous work.