# Towards Energy Efficient Federated Learning over 5G+ Mobile Devices

arXiv: https://arxiv.org/abs/2101.04866 · HTML: https://mlsanity.com/paper/2101.04866/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 13 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices](https://mlsanity.com/paper/2012.11804/) (2020)
- [Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission](https://mlsanity.com/paper/2012.11070/) (2020)

## Built on by (most cited first, 16 of 16)

- [Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks](https://mlsanity.com/paper/2303.15991/) (2023)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Service Delay Minimization for Federated Learning over Mobile Devices](https://mlsanity.com/paper/2205.09868/) (2022)
- [Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation](https://mlsanity.com/paper/2208.07237/) (2022)
- [Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy](https://mlsanity.com/paper/2304.07460/) (2023)
- [COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms](https://mlsanity.com/paper/2410.19375/) (2024)
- [FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing](https://mlsanity.com/paper/2111.06146/) (2021)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Energy-Aware Deep Learning on Resource-Constrained Hardware](https://mlsanity.com/paper/2505.12523/) (2025)
- [To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices](https://mlsanity.com/paper/2111.00637/) (2021)
- [Efficient Split Federated Learning for Large Language Models over Communication Networks](https://mlsanity.com/paper/2504.14667/) (2025)
- [The Two Faces of AI in Green Mobile Computing: A Literature Review](https://mlsanity.com/paper/2308.04436/) (2023)
- [Towards Energy-Aware Federated Learning via MARL: A Dual-Selection Approach for Model and Client](https://mlsanity.com/paper/2405.08183/) (2024)
- [Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments](https://mlsanity.com/paper/2505.06268/) (2025)
- [A Methodology to Assess Power Modeling in Energy-Aware Federated Learning on Heterogeneous Mobile Devices](https://mlsanity.com/paper/2605.27601/) (2026)
- [Range Penalization: Theoretical Insights with Applications in Federated Learning](https://mlsanity.com/paper/2606.10916/) (2026)

## 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.