Convergence Time Optimization for Federated Learning over Wireless Networks
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Abstract (arXiv)
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 performance and convergence time will be significantly affected by the user selection scheme. Therefore, it is necessary to design an appropriate user selection scheme that enables users of higher importance to be selected 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 while optimizing the FL performance. 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 its 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 enhance its global FL model and improve the FL convergence speed and performance.
Key facts. Cited by 81 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jan 2020.
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Citing papers per year: 2021: 34; 2022: 13; 2023: 19; 2024: 6; 2025: 5; 2026: 4.
81 papers.
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- 2023Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy9 Apr 2023
- 2023AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices8 Jan 2023
- 2022Graph Federated Learning for CIoT Devices in Smart Home Applications29 Dec 2022
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50 newest of 81 All 81 (JSON)
- 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
- 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
- 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 79
- 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
- 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
- 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
- 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
- 2021Applications and Techniques for Fast Machine Learning in Sciencecited by 32
- 2021Cost-Effective Federated Learning in Mobile Edge Networkscited by 25
- 2021Low-Latency Federated Learning over Wireless Channels with Differential Privacycited by 24
- 2022FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentationcited by 24
- 2021Decentralized Inference with Graph Neural Networks in Wireless Communication Systemscited by 18
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- 2026Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Data13 Sep 2026
- 2026Hidden in Rounds: Predicting the Time Cost of 802.11 Contention in Federated Learning11 Sep 2026
- 2026From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning9 Jun 2026
- 2026Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networks24 Feb 2026
- 2025On Transferring, Merging, and Splitting Task-Oriented Network Digital Twins2 Sep 2025
- 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning9 Jun 2025
- 2025Federated Learning-Distillation Alternation for Resource-Constrained IoT26 May 2025
- 2025Energy-Efficient Federated Learning for AIoT using Clustering Methods14 May 2025
- 2025Stackelberg Game Based Performance Optimization in Digital Twin Assisted Federated Learning over NOMA Networks3 Jan 2025
- 2024Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learning17 Aug 2024
- 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing11 Jul 2024
- 2024Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks22 Apr 2024
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