Towards Energy Efficient Federated Learning over 5G+ Mobile Devices
Not on the map. Why?
Abstract (arXiv)
The continuous convergence of machine learning algorithms, 5G and beyond (5G+) wireless communications, and artificial intelligence (AI) hardware implementation hastens the birth of federated learning (FL) over 5G+ mobile devices, which pushes AI functions to mobile devices and initiates a new era of on-device AI applications. Despite the remarkable progress made in FL, huge energy consumption is one of the most significant obstacles restricting the development of FL over battery-constrained 5G+ mobile devices. To address this issue, in this paper, we investigate how to develop energy efficient FL over 5G+ mobile devices by making a trade-off between energy consumption for "working" (i.e., local computing) and that for "talking" (i.e., wireless communications) in order to boost the overall energy efficiency. Specifically, we first examine energy consumption models for graphics processing unit (GPU) computation and wireless transmissions. Then, we overview the state of the art of integrating FL procedure with energy-efficient learning techniques (e.g., gradient sparsification, weight quantization, pruning, etc.). Finally, we present several potential future research directions for FL over 5G+ mobile devices from the perspective of energy efficiency.
Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 7 of its 19 references that are in the map. First posted on arXiv 13 Jan 2021.
Builds on
7 of 19 references.
References · 7
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
- 2017Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourcited by 1,479
- 2018Federated Learning for Mobile Keyboard Predictioncited by 565
- 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 157
- 2020To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devicescited by 45
- 2020Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmissioncited by 22
Built on by
Citing papers per year: 2021: 2; 2022: 2; 2023: 5; 2024: 2; 2025: 3; 2026: 2.
16 papers.
Sort:
- 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
- 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
- 2022Service Delay Minimization for Federated Learning over Mobile Devicescited by 9
- 2022Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computationcited by 6
- 2023Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacycited by 5
- 2024COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platformscited by 5
- 2021FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computingcited by 4
- 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
- 2025Energy-Aware Deep Learning on Resource-Constrained Hardwarecited by 3
- 2021To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devicescited by 2
- 2025Efficient Split Federated Learning for Large Language Models over Communication Networkscited by 1
- 2023The Two Faces of AI in Green Mobile Computing: A Literature Reviewcited by 0
4 more
- 2024Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Clientcited by 0
- 2025Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environmentscited by 0
- 2026A Methodology to Assess Power Modeling in Energy-Aware Federated Learning on Heterogeneous Mobile Devicescited by 0
- 2026Range Penalization: Theoretical Insights with Applications in Federated Learningcited by 0
- 2026Range Penalization: Theoretical Insights with Applications in Federated Learning9 Jun 2026
- 2026A Methodology to Assess Power Modeling in Energy-Aware Federated Learning on Heterogeneous Mobile Devices26 May 2026
- 2025Energy-Aware Deep Learning on Resource-Constrained Hardware18 May 2025
- 2025Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments6 May 2025
- 2025Efficient Split Federated Learning for Large Language Models over Communication Networks20 Apr 2025
- 2024COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms25 Oct 2024
- 2024Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client13 May 2024
- 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights7 Dec 2023
- 2023The Two Faces of AI in Green Mobile Computing: A Literature Review21 Jul 2023
- 2023Decentralized Federated Learning: A Survey and Perspective2 Jun 2023
- 2023Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy15 Apr 2023
- 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks26 Mar 2023
4 more
- 2022Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation15 Aug 2022
- 2022Service Delay Minimization for Federated Learning over Mobile Devices19 May 2022
- 2021FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing11 Nov 2021
- 2021To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices1 Nov 2021
- 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
- 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
- 2022Service Delay Minimization for Federated Learning over Mobile Devicescited by 9
- 2022Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computationcited by 6
- 2023Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacycited by 5
- 2024COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platformscited by 5
- 2021FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computingcited by 4
- 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
- 2025Energy-Aware Deep Learning on Resource-Constrained Hardwarecited by 3
- 2021To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devicescited by 2
- 2025Efficient Split Federated Learning for Large Language Models over Communication Networkscited by 1
- 2023The Two Faces of AI in Green Mobile Computing: A Literature Reviewcited by 0
12 most cited of 16 All 16 (JSON)
- 2026Range Penalization: Theoretical Insights with Applications in Federated Learning9 Jun 2026
- 2026A Methodology to Assess Power Modeling in Energy-Aware Federated Learning on Heterogeneous Mobile Devices26 May 2026
- 2025Energy-Aware Deep Learning on Resource-Constrained Hardware18 May 2025
- 2025Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments6 May 2025
- 2025Efficient Split Federated Learning for Large Language Models over Communication Networks20 Apr 2025
- 2024COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms25 Oct 2024
- 2024Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client13 May 2024
- 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights7 Dec 2023
- 2023The Two Faces of AI in Green Mobile Computing: A Literature Review21 Jul 2023
- 2023Decentralized Federated Learning: A Survey and Perspective2 Jun 2023
- 2023Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy15 Apr 2023
- 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks26 Mar 2023
12 newest of 16 All 16 (JSON)