Abstract
Federated Learning (FL) is a distributed machine learning approach which is suitable for edge computing environment. While in this environment, how to take full advantage of the computing resources on end devices and edge servers is still a difficult problem. Especially for the synchronous federated learning, computing resources among different participants will lead to extra time cost and cause resource waste. In this paper, we try to reduce the time cost and the computing resource waste by using model splitting and task scheduling. We first establish the mathematical model and find it can not be solved directly. Then we design our algorithm which we name as the Federated Learning Offloading Acceleration (FLOA) algorithm to obtain a sub-optimal solution. The FLOA algorithm first uses the Partition Points Selection method to reduce the size of the solution space, then proposes a task offloading method based on matching theory. Experiments and simulations show that compared to the other three calculation methods, the single iteration time is reduced by \(47\%\), \(28\%\), \(14\%\) under our algorithm in turn.
The Work is Supported by Major Science and Technology Projects in Anhui Province (202003a05020009).
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Fang, C., Shi, L., Shi, Y., Xu, J., Ding, X. (2022). Synchronous Federated Learning Latency Optimization Based on Model Splitting. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13473. Springer, Cham. https://doi.org/10.1007/978-3-031-19211-1_41
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