1. Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
- Author
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Sheng Zhou, Wenqi Shi, Lu Geng, Miao Jiang, and Zhisheng Niu
- Subjects
Signal Processing (eess.SP) ,FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer science ,Distributed computing ,Computer Science - Information Theory ,02 engineering and technology ,Upper and lower bounds ,Scheduling (computing) ,Machine Learning (cs.LG) ,Computer Science - Networking and Internet Architecture ,0202 electrical engineering, electronic engineering, information engineering ,FOS: Electrical engineering, electronic engineering, information engineering ,Wireless ,Electrical and Electronic Engineering ,Latency (engineering) ,Electrical Engineering and Systems Science - Signal Processing ,Networking and Internet Architecture (cs.NI) ,business.industry ,Applied Mathematics ,Information Theory (cs.IT) ,020206 networking & telecommunications ,Computer Science Applications ,Bandwidth allocation ,Rate of convergence ,Resource allocation ,business ,Communication channel - Abstract
In federated learning (FL), devices contribute to the global training by uploading their local model updates via wireless channels. Due to limited computation and communication resources, device scheduling is crucial to the convergence rate of FL. In this paper, we propose a joint device scheduling and resource allocation policy to maximize the model accuracy within a given total training time budget for latency constrained wireless FL. A lower bound on the reciprocal of the training performance loss, in terms of the number of training rounds and the number of scheduled devices per round, is derived. Based on the bound, the accuracy maximization problem is solved by decoupling it into two sub-problems. First, given the scheduled devices, the optimal bandwidth allocation suggests allocating more bandwidth to the devices with worse channel conditions or weaker computation capabilities. Then, a greedy device scheduling algorithm is introduced, which in each step selects the device consuming the least updating time obtained by the optimal bandwidth allocation, until the lower bound begins to increase, meaning that scheduling more devices will degrade the model accuracy. Experiments show that the proposed policy outperforms state-of-the-art scheduling policies under extensive settings of data distributions and cell radius., Comment: submitted to IEEE Trans. Wireless Communications
- Published
- 2020
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