Convergence Time Optimization for Federated Learning Over Wireless Networks

By Mingzhe Chen, H. Vincent Poor, Walid Saad, Shuguang Cui. IEEE Transactions on Wireless Communications, 2020

340 citations recorded by the source corpus.

In this paper, the convergence time of federated learning (FL), when deployed over a realistic wireless network, is studied. In particular, a wireless network is considered in which wireless users transmit their local FL models (trained using their locally collected data) to a base station (BS). The BS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all users. Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected to transmit their local FL model parameters to the BS at each learning step. Moreover, since each user has unique training data samples, the BS prefers to include all local user FL models to generate a converged global FL model. Hence, the FL training loss and convergence time will be significantly affected by the user selection scheme. Therefore, it is necessary to design an appropriate user selection scheme that can select the users who can contribute toward improving the FL convergence speed more frequently. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize the FL convergence time and the FL training loss. To solve this problem, a probabilistic user selection scheme is proposed such that the BS is connected to the users whose local FL models have significant effects on the global FL model with high probabilities. Given the user selection policy, the uplink RB allocation can be determined. To further reduce the FL convergence time, artificial neural networks (ANNs) are used to estimate the local FL models of the users that are not allocated any RBs for local FL model transmission at each given learning step, which enables the BS to improve the global model, the FL convergence speed, and the training loss. Simulation results show that the proposed approach can reduce the FL convergence time by up to 56% and improve the accuracy of identifying handwritten digits by up to 3%, compared to a standard FL algorithm.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/cd68b4c92463d956f62a0a2b93211d78818df2c5

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Citations in the corpus

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, background): “Speciﬁcally, for edge training systems, we need to jointly optimize the subcarrier and bandwidth allocation [241], [88], [90], transmit power and receive beamforming [31], [48], [178], passive beamforming at RIS [48], [178], device selection [31], [242] and activity detection [76], local updates…”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, neutral): “Moreover, a probabilistic device selection scheme was proposed in [151] aiming at choosing the devices, whose local learning models have”
Federated Learning for 6G: Applications, Challenges, and Opportunities (2021, background): “Many studies of FL convergence are based on the assumption of a convex loss function [90,91].”
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges (2021, background): “The authors in [53]–[56] study the use of gradient statistics to optimize the set of devices that participate in FL at each training round.”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, background): “In contrast, the authors in [125] propose a neural network based approach to estimate the local models of FL partici-”
Communication-efficient federated learning (2021, positive): “However, the majority of these works focus on optimization of FL in a single aspect such as device selection and scheduling (11–13), FL model parameter update and transmission (14–18), or network resource management (20–22).”
Agentic AI: Autonomous Intelligence for Complex Goals—A Comprehensive Survey (2025, neutral): “Quick learning rate and quick convergence are always preferred as they enable the AI to work effectively in dynamic environments requiring continual learning [40], [41].”
Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective (2020, background): “, [3], [29]–[33], have been proposed to either minimize the learning loss or the training time.”