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FLEE: A Hierarchical Federated Learning Framework for Distributed Deep Neural Network over Cloud, Edge, and End Device

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2022年第5期13卷 1–24页

作者： Zhong, Zhengyi Bao, Weidong Wang, Ji Zhu, Xiaomin Zhang, Xiongtao Natl Univ Def Technol Coll Syst Engn 137 Yanwachi St Changsha 410003 Hunan Peoples R China

With the development of smart devices, the computing capabilities of portable end devices such as mobile phones have been greatly enhanced. Meanwhile, traditional cloud computing faces great challenges caused by privacy-leakage and time-delay problems, there is a trend to push models down to edges and end devices. However, due to the limitation of computing resource, it is difficult for end devices to complete complex computing tasks alone. Therefore, this article divides the model into two parts and deploys them on multiple end devices and edges, respectively. Meanwhile, an early exit is set to reduce computing resource overhead, forming a hierarchical distributed architecture. In order to enable the distributed model to continuously evolve by using new data generated by end devices, we comprehensively consider various data distributions on end devices and edges, proposing a hierarchical federated learning framework FLEE, which can realize dynamical updates of models without redeploying them. Through image and sentence classification experiments, we verify that it can improve model performances under all kinds of data distributions, and prove that compared with other frameworks, the models trained by FLEE consume less global computing resource in the inference stage.

关键词： Federated learning early exit of inference distributed neural network

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