Delay Minimization for Federated Learning Over Wireless Communication Networks
Delay Minimization for Federated Learning Over Wireless Communication Networks
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发表时间：
2020-07
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通讯作者：
Zhaohui Yang;Mingzhe Chen;W. Saad;C. Hong;M. Shikh-Bahaei;H. Poor;Shuguang Cui
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作者：
Zhaohui Yang;Mingzhe Chen;W. Saad;C. Hong;M. Shikh-Bahaei;H. Poor;Shuguang Cui
研究了无线通信网络上联邦学习（FL）的时延最小化问题。在所考虑的模型中，每个用户利用有限的本地计算资源利用其收集的数据训练本地FL模型，然后将训练好的FL模型参数发送到聚合本地FL模型的基站（BS），并将聚合后的FL模型广播给所有用户。由于FL涉及到用户与BS之间的学习模型交换，因此计算延迟和通信延迟都取决于所需的学习精度水平，这影响了FL算法的收敛速度。将该联合学习与交流问题表述为时滞最小化问题，并证明了目标函数是学习精度的凸函数。然后，提出了一种二分搜索算法来获得最优解。仿真结果表明，该算法与传统的FL方法相比，时延降低了27.3%。
In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated. In the considered model, each user exploits limited local computational resources to train a local FL model with its collected data and, then, sends the trained FL model parameters to a base station (BS) which aggregates the local FL models and broadcasts the aggregated FL model back to all the users. Since FL involves learning model exchanges between the users and the BS, both computation and communication latencies are determined by the required learning accuracy level, which affects the convergence rate of the FL algorithm. This joint learning and communication problem is formulated as a delay minimization problem, where it is proved that the objective function is a convex function of the learning accuracy. Then, a bisection search algorithm is proposed to obtain the optimal solution. Simulation results show that the proposed algorithm can reduce delay by up to 27.3% compared to conventional FL methods.