通过用户模型相似性减少边缘联邦学习通信开销
FedSAUC: A Similarity-Aware Update Control for Communication-Efficient Federated Learning in Edge Computing
- 按用户模型相似性聚类，仅选代表性设备上传更新
- 在保持精度的前提下，显著降低通信频次和能耗
- 适合电池供电的边缘设备，如手机与物联网设备
联邦学习是一种分布式机器学习框架，可在不上传敏感数据的情况下协同训练全局模型，常用于智能手机、可穿戴设备和物联网设备等边缘设备。然而，这些设备多依赖电池供电，联邦学习的更新过程会持续消耗电量和带宽。本文提出一种名为 FedSAUC 的更新控制机制，通过考虑用户行为（模型）的相似性，在服务器端使用聚类算法将模型相似的设备分组，并为每组选择代表设备进行信息更新。我们在边缘设备上搭建了测试平台以验证性能。实验结果表明，该机制在长期训练中不会影响模型精度，同时有效降低了通信开销。
原文摘要 · Abstract (English)
Federated learning is a distributed machine learning framework to collaboratively train a global model without uploading privacy-sensitive data onto a centralized server. Usually, this framework is applied to edge devices such as smartphones, wearable devices, and Internet of Things (IoT) devices which closely collect information from users. However, these devices are mostly battery-powered. The update procedure of federated learning will constantly consume the battery power and the transmission bandwidth. In this work, we propose an update control for federated learning, FedSAUC, by considering the similarity of users' behaviors (models). At the server side, we exploit clustering algorithms to group devices with similar models. Then we select some representatives for each cluster to update information to train the model. We also implemented a testbed prototyping on edge devices for validating the performance. The experimental results show that this update control will not affect the training accuracy in the long run.
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