Abstract
Federated learning is considered to be a privacy-preserving collaborative machine learning training method. However, due to the general limitation of the computing ability of the terminal device, the training efficiency becomes an issue when training some complex deep neural network models. On the other hand, edges, the nearby stationary devices with higher computational capacity, might serve as a help. This paper presents the design of a component-based federated learning framework, which facilitates the offloading of training layers to nearby edge devices while preserving the users’ privacy. We conduct an empirical study on a classic convolutional neural network to validate our framework. Experiments show that this method can effectively shorten the time cost for mobile terminals to perform local training in the federated learning process.
This work was supported by Project 61902333 supported by National Natural Science Foundation of China, by the Key Area R&D Program of Guangdong Province with grant No. 2018B030338001, by the Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS).
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Shi, Y., Duan, H., Chi, Y., Gai, K., Cai, W. (2020). Edge-Assisted Federated Learning: An Empirical Study from Software Decomposition Perspective. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12453. Springer, Cham. https://doi.org/10.1007/978-3-030-60239-0_14
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DOI: https://doi.org/10.1007/978-3-030-60239-0_14
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