Energy Efficient Federated Learning Over Wireless Communication Networks
Energy Efficient Federated Learning Over Wireless Communication Networks
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DOI：
10.1109/twc.2020.3037554
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发表时间：
2021-03-01
影响因子：
10.4
通讯作者：
Shikh-Bahaei, Mohammad
中科院分区：
文献类型：
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作者：
Yang, Zhaohui;Chen, Mingzhe;Shikh-Bahaei, Mohammad
研究了无线通信网络中联邦学习的能量有效传输和计算资源分配问题。在所考虑的模型中，每个用户利用有限的本地计算资源来用其收集的数据训练本地FL模型，然后将训练的FL模型发送到基站（BS），基站（BS）聚合本地FL模型并将其广播回所有用户。由于FL涉及用户和BS之间的学习模型的交换，因此计算和通信延迟都由学习精度水平确定。同时，由于无线用户的能量预算有限，在FL过程中必须同时考虑本地计算能量和传输能量。该联合学习和通信问题被公式化为优化问题，其目标是在延迟约束下最小化系统的总能耗。为了解决这个问题，提出了一种迭代算法，其中每一步都推导出时间分配、带宽分配、功率控制、计算频率和学习精度的封闭解。由于迭代算法需要一个初始可行解，我们构造了完成时间最小化问题，并提出了一个基于二分法的算法来获得最优解，这是一个可行的解决方案，以原来的能量最小化问题。数值结果表明，所提出的算法可以减少高达59.5%的能量消耗相比，传统的FL方法。
In this paper, the problem of energy efficient transmission and computation resource allocation 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 to a base station (BS) which aggregates the local FL model and broadcasts it back to all of the users. Since FL involves an exchange of a learning model between users and the BS, both computation and communication latencies are determined by the learning accuracy level. Meanwhile, due to the limited energy budget of the wireless users, both local computation energy and transmission energy must be considered during the FL process. This joint learning and communication problem is formulated as an optimization problem whose goal is to minimize the total energy consumption of the system under a latency constraint. To solve this problem, an iterative algorithm is proposed where, at every step, closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy are derived. Since the iterative algorithm requires an initial feasible solution, we construct the completion time minimization problem and a bisection-based algorithm is proposed to obtain the optimal solution, which is a feasible solution to the original energy minimization problem. Numerical results show that the proposed algorithms can reduce up to 59.5% energy consumption compared to the conventional FL method.