# Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission

arXiv: https://arxiv.org/abs/2012.11070 · HTML: https://mlsanity.com/paper/2012.11070/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 23 of 23)

- [Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks](https://mlsanity.com/paper/2303.15991/) (2023)
- [Towards Energy Efficient Federated Learning over 5G+ Mobile Devices](https://mlsanity.com/paper/2101.04866/) (2021)
- [Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design](https://mlsanity.com/paper/2207.09387/) (2022)
- [Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection](https://mlsanity.com/paper/2401.16685/) (2024)
- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability](https://mlsanity.com/paper/2310.17944/) (2023)
- [Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation](https://mlsanity.com/paper/2208.07237/) (2022)
- [FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Models](https://mlsanity.com/paper/2312.15926/) (2023)
- [Multiple Access in the Era of Distributed Computing and Edge Intelligence](https://mlsanity.com/paper/2403.07903/) (2024)
- [NestQuant: Post-Training Integer-Nesting Quantization for On-Device DNN](https://mlsanity.com/paper/2506.17870/) (2025)
- [Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing](https://mlsanity.com/paper/2405.18707/) (2024)
- [Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learning](https://mlsanity.com/paper/2501.01828/) (2025)
- [Towards Heterogeneity-Aware and Energy-Efficient Topology Optimization for Decentralized Federated Learning in Edge Environment](https://mlsanity.com/paper/2508.08278/) (2025)
- [On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks](https://mlsanity.com/paper/2403.04430/) (2024)
- [Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction](https://mlsanity.com/paper/2507.17768/) (2025)
- [Over-the-Air Federated Averaging with Limited Power and Privacy Budgets](https://mlsanity.com/paper/2305.03547/) (2023)
- [Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization](https://mlsanity.com/paper/2402.12957/) (2024)
- [FedShift: Robust Federated Learning Aggregation Scheme in Resource Constrained Environment via Weight Shifting](https://mlsanity.com/paper/2402.01070/) (2024)
- [A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control](https://mlsanity.com/paper/2504.01752/) (2025)
- [Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design](https://mlsanity.com/paper/2508.01745/) (2025)
- [Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devices](https://mlsanity.com/paper/2509.12814/) (2025)
- [Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation](https://mlsanity.com/paper/2606.10774/) (2026)
- [GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks](https://mlsanity.com/paper/2607.29659/) (2026)

## Abstract (arXiv)

Federated learning (FL) is a popular collaborative distributed machine learning paradigm across mobile devices. However, practical FL over resource constrained mobile devices confronts multiple challenges, e.g., the local on-device training and model updates in FL are power hungry and radio resource intensive for mobile devices. To address these challenges, in this paper, we attempt to take FL into the design of future wireless networks and develop a novel joint design of wireless transmission and weight quantization for energy efficient FL over mobile devices. Specifically, we develop flexible weight quantization schemes to facilitate on-device local training over heterogeneous mobile devices. Based on the observation that the energy consumption of local computing is comparable to that of model updates, we formulate the energy efficient FL problem into a mixed-integer programming problem where the quantization and spectrum resource allocation strategies are jointly determined for heterogeneous mobile devices to minimize the overall FL energy consumption (computation + transmissions) while guaranteeing model performance and training latency. Since the optimization variables of the problem are strongly coupled, an efficient iterative algorithm is proposed, where the bandwidth allocation and weight quantization levels are derived. Extensive simulations are conducted to verify the effectiveness of the proposed scheme.