Energy Efficient Federated Learning Over Wireless Communication Networks
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Abstract (arXiv)
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.
Key facts. Cited by 181 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Nov 2019.
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Citing papers per year: 2021: 32; 2022: 42; 2023: 39; 2024: 34; 2025: 19; 2026: 15.
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50 newest of 181 All 181 (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
- 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
- 2023A Systematic Review of Green AIcited by 77
- 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
- 2022Communication-Efficient Adaptive Federated Learningcited by 55
- 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 50
- 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
- 2024Hierarchical Split Federated Learning: Convergence Analysis and System Optimizationcited by 38
- 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
- 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
- 2022Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimizationcited by 28
12 most cited of 180 All 180 (JSON)
- 2026Constrained Bayesian Optimization for Hierarchical Federated Learning in IoT Networks for Plant Disease Classification6 Sep 2026
- 2026Differentially private federated learning with Byzantine-robust aggregation: A cross-domain framework for secure model training in banking and healthcare systems2 Sep 2026
- 2026GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks31 Jul 2026
- 2026Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks29 May 2026
- 2026A Methodology to Assess Power Modeling in Energy-Aware Federated Learning on Heterogeneous Mobile Devices26 May 2026
- 2026Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learning25 May 2026
- 2026Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptation27 Apr 2026
- 2026Cooperate to Compete: Strategic Data Generation and Incentivization Framework for Coopetitive Cross-Silo Federated Learning16 Apr 2026
- 2026Energy-Efficient Hierarchical Federated Anomaly Detection for the Internet of Underwater Things via Selective Cooperative Aggregation25 Mar 2026
- 2026A Theoretical Framework for Energy-Aware Gradient Pruning in Federated Learning23 Mar 2026
- 2026Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learning11 Mar 2026
- 2026Communication-Efficient Quantum Federated Learning over Large-Scale Wireless Networks1 Mar 2026
12 newest of 180 All 180 (JSON)