Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning

By Wenqi Shi, Sheng Zhou, Zhisheng Niu, Miao Jiang, Lu Geng. IEEE Transactions on Wireless Communications, 2020

357 citations recorded by the source corpus.

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 selects the device consuming the least updating time obtained by the optimal bandwidth allocation in each step, 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.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/b6d758acb3384eb6603a2d52f6d5666511e8c1c8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Distributed Learning in Wireless Networks: Recent Progress and Future Challenges (2021, methodology): “The authors in [69] jointly optimized device scheduling and resource allocation policies to”
Federated Learning for 6G: Applications, Challenges, and Opportunities (2021, background): “In addition, scheduling only a fraction of all devices at each round of FL uploading is a promising alternative [36,37].”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, background): “The trade-off between the convergence speed and the per-round latency was revealed in [242] based on the key observation that more scheduled devices yield faster convergence rate while prolonging the time of uploading the local updates at each iteration due to limited radio resources.”
Federated Learning for Smart Healthcare: A Survey (2021, background): “Another joint optimization of device scheduling and resource allocation framework is investigated in [42].”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, neutral): “In [170] and [156], a greedy device scheduling algorithm and a Gibbs sampling based device selection method were devised,”