# To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices

arXiv: https://arxiv.org/abs/2012.11804 · HTML: https://mlsanity.com/paper/2012.11804/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 30 of 46)

- [Efficient Device Scheduling with Multi-Job Federated Learning](https://mlsanity.com/paper/2112.05928/) (2021)
- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [Multi-Job Intelligent Scheduling with Cross-Device Federated Learning](https://mlsanity.com/paper/2211.13430/) (2022)
- [Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity](https://mlsanity.com/paper/2206.02633/) (2022)
- [Towards Energy Efficient Federated Learning over 5G+ Mobile Devices](https://mlsanity.com/paper/2101.04866/) (2021)
- [Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions](https://mlsanity.com/paper/2207.09611/) (2022)
- [Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization](https://mlsanity.com/paper/2212.08272/) (2022)
- [Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm](https://mlsanity.com/paper/2404.00836/) (2024)
- [Federated Learning with Flexible Control](https://mlsanity.com/paper/2212.08496/) (2022)
- [AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices](https://mlsanity.com/paper/2301.03062/) (2023)
- [FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy](https://mlsanity.com/paper/2310.09789/) (2023)
- [MobiLLM: Enabling LLM Fine-Tuning on the Mobile Device via Server Assisted Side Tuning](https://mlsanity.com/paper/2502.20421/) (2025)
- [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)
- [Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework](https://mlsanity.com/paper/2301.00489/) (2023)
- [Towards Communication-Learning Trade-off for Federated Learning at the Network Edge](https://mlsanity.com/paper/2205.14271/) (2022)
- [A Fast Blockchain-based Federated Learning Framework with Compressed Communications](https://mlsanity.com/paper/2208.06095/) (2022)
- [Joint Optimization of Energy Consumption and Completion Time in Federated Learning](https://mlsanity.com/paper/2209.14900/) (2022)
- [Social-Aware Clustered Federated Learning with Customized Privacy Preservation](https://mlsanity.com/paper/2212.13992/) (2022)
- [FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing](https://mlsanity.com/paper/2111.06146/) (2021)
- [PAE MobiLLM: Privacy-Aware and Efficient LLM Fine-Tuning on the Mobile Device via Additive Side-Tuning](https://mlsanity.com/paper/2507.01216/) (2025)
- [Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning](https://mlsanity.com/paper/2212.09483/) (2022)
- [Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks](https://mlsanity.com/paper/2412.00090/) (2024)
- [OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning](https://mlsanity.com/paper/2105.00602/) (2021)
- [Management of Resource at the Network Edge for Federated Learning](https://mlsanity.com/paper/2107.03428/) (2021)
- [To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices](https://mlsanity.com/paper/2111.00637/) (2021)
- [Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression](https://mlsanity.com/paper/2409.04022/) (2024)
- [Communication Efficient Distributed Learning over Wireless Channels](https://mlsanity.com/paper/2209.01682/) (2022)
- [Scheduling Algorithms for Federated Learning with Minimal Energy Consumption](https://mlsanity.com/paper/2209.06210/) (2022)
- [Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes](https://mlsanity.com/paper/2402.03770/) (2024)

## Abstract (arXiv)

Recent advances in machine learning, wireless communication, and mobile hardware technologies promisingly enable federated learning (FL) over massive mobile edge devices, which opens new horizons for numerous intelligent mobile applications. Despite the potential benefits, FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training, raising great challenges to battery constrained mobile devices. In this work, we target at improving the energy efficiency of FL over mobile edge networks to accommodate heterogeneous participating devices without sacrificing the learning performance. To this end, we develop a convergence-guaranteed FL algorithm enabling flexible communication compression. Guided by the derived convergence bound, we design a compression control scheme to balance the energy consumption of local computing (i.e., "working") and wireless communication (i.e., "talking") from the long-term learning perspective. In particular, the compression parameters are elaborately chosen for FL participants adapting to their computing and communication environments. Extensive simulations are conducted using various datasets to validate our theoretical analysis, and the results also demonstrate the efficacy of the proposed scheme in energy saving.