# Towards Energy Efficient Distributed Federated Learning for 6G Networks

arXiv: https://arxiv.org/abs/2201.08270 · HTML: https://mlsanity.com/paper/2201.08270/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 19 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [Wireless Communications for Collaborative Federated Learning](https://mlsanity.com/paper/2006.02499/) (2020)
- [GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning](https://mlsanity.com/paper/1909.00047/) (2019)
- [Detection of Unauthorized IoT Devices Using Machine Learning Techniques](https://mlsanity.com/paper/1709.04647/) (2017)
- [Envisioning Device-to-Device Communications in 6G](https://mlsanity.com/paper/1912.05771/) (2019)

## Built on by (most cited first, 7 of 7)

- [AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends](https://mlsanity.com/paper/2208.10921/) (2022)
- [Integration of Agentic AI with 6G Networks for Mission-Critical Applications: Use-case and Challenges](https://mlsanity.com/paper/2502.13476/) (2025)
- [SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks](https://mlsanity.com/paper/2211.11321/) (2022)
- [SelfFed: Self-Supervised Federated Learning for Data Heterogeneity and Label Scarcity in Medical Images](https://mlsanity.com/paper/2307.01514/) (2023)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [DART: A Server-side Plug-in for Resource-efficient Robust Federated Learning](https://mlsanity.com/paper/2508.17381/) (2025)
- [Closing the Generalization Gap in Parameter-efficient Federated Edge Learning](https://mlsanity.com/paper/2511.23282/) (2025)

## Abstract (arXiv)

The provision of communication services via portable and mobile devices, such as aerial base stations, is a crucial concept to be realized in 5G/6G networks. Conventionally, IoT/edge devices need to transmit the data directly to the base station for training the model using machine learning techniques. The data transmission introduces privacy issues that might lead to security concerns and monetary losses. Recently, Federated learning was proposed to partially solve privacy issues via model-sharing with base station. However, the centralized nature of federated learning only allow the devices within the vicinity of base stations to share the trained models. Furthermore, the long-range communication compels the devices to increase transmission power, which raises the energy efficiency concerns. In this work, we propose distributed federated learning (DBFL) framework that overcomes the connectivity and energy efficiency issues for distant devices. The DBFL framework is compatible with mobile edge computing architecture that connects the devices in a distributed manner using clustering protocols. Experimental results show that the framework increases the classification performance by 7.4% in comparison to conventional federated learning while reducing the energy consumption.