# Energy-Efficient Radio Resource Allocation for Federated Edge Learning

arXiv: https://arxiv.org/abs/1907.06040 · HTML: https://mlsanity.com/paper/1907.06040/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2019.

## Built on by (most cited first, 30 of 39)

- [Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis](https://mlsanity.com/paper/2101.12704/) (2021)
- [Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints](https://mlsanity.com/paper/2106.00490/) (2021)
- [Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation](https://mlsanity.com/paper/2103.01953/) (2021)
- [Joint Optimization of Communications and Federated Learning Over the Air](https://mlsanity.com/paper/2104.03490/) (2021)
- [Accelerated Gradient Descent Learning over Multiple Access Fading Channels](https://mlsanity.com/paper/2107.12452/) (2021)
- [Efficient Federated Meta-Learning over Multi-Access Wireless Networks](https://mlsanity.com/paper/2108.06453/) (2021)
- [Federated Learning over Wireless IoT Networks with Optimized Communication and Resources](https://mlsanity.com/paper/2110.11775/) (2021)
- [Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach](https://mlsanity.com/paper/2102.07972/) (2021)
- [Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks](https://mlsanity.com/paper/2101.03627/) (2021)
- [Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Scheduling](https://mlsanity.com/paper/2109.06710/) (2021)
- [Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality](https://mlsanity.com/paper/2211.08705/) (2022)
- [Federated Learning over Noisy Channels: Convergence Analysis and Design Examples](https://mlsanity.com/paper/2101.02198/) (2021)
- [Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learning](https://mlsanity.com/paper/2106.12561/) (2021)
- [Energy-Harvesting Distributed Machine Learning](https://mlsanity.com/paper/2102.05639/) (2021)
- [Data-Aware Device Scheduling for Federated Edge Learning](https://mlsanity.com/paper/2102.09491/) (2021)
- [Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things](https://mlsanity.com/paper/2402.02506/) (2024)
- [Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach](https://mlsanity.com/paper/2405.12046/) (2024)
- [Sustainable Federated Learning](https://mlsanity.com/paper/2102.11274/) (2021)
- [Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation](https://mlsanity.com/paper/2302.06730/) (2023)
- [Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networks](https://mlsanity.com/paper/2107.03520/) (2021)
- [An Optimization Framework for Federated Edge Learning](https://mlsanity.com/paper/2111.13526/) (2021)
- [A Systematic Literature Review on Client Selection in Federated Learning](https://mlsanity.com/paper/2306.04862/) (2023)
- [Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approach](https://mlsanity.com/paper/2406.09776/) (2024)
- [Energy-Aware Deep Learning on Resource-Constrained Hardware](https://mlsanity.com/paper/2505.12523/) (2025)
- [Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learning](https://mlsanity.com/paper/2104.05509/) (2021)
- [Management of Resource at the Network Edge for Federated Learning](https://mlsanity.com/paper/2107.03428/) (2021)
- [On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations](https://mlsanity.com/paper/2208.11848/) (2022)
- [A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks](https://mlsanity.com/paper/2308.10664/) (2023)
- [Nothing Wasted: Full Contribution Enforcement in Federated Edge Learning](https://mlsanity.com/paper/2110.08330/) (2021)
- [User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks](https://mlsanity.com/paper/2309.09253/) (2023)

## Abstract (arXiv)

Edge machine learning involves the development of learning algorithms at the network edge to leverage massive distributed data and computation resources. Among others, the framework of federated edge learning (FEEL) is particularly promising for its data-privacy preservation. FEEL coordinates global model training at a server and local model training at edge devices over wireless links. In this work, we explore the new direction of energy-efficient radio resource management (RRM) for FEEL. To reduce devices' energy consumption, we propose energy-efficient strategies for bandwidth allocation and scheduling. They adapt to devices' channel states and computation capacities so as to reduce their sum energy consumption while warranting learning performance. In contrast with the traditional rate-maximization designs, the derived optimal policies allocate more bandwidth to those scheduled devices with weaker channels or poorer computation capacities, which are the bottlenecks of synchronized model updates in FEEL. On the other hand, the scheduling priority function derived in closed form gives preferences to devices with better channels and computation capacities. Substantial energy reduction contributed by the proposed strategies is demonstrated in learning experiments.