# D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless Network Edge

arXiv: https://arxiv.org/abs/2001.11342 · HTML: https://mlsanity.com/paper/2001.11342/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 5 of 5)

- [Over-the-Air Decentralized Federated Learning](https://mlsanity.com/paper/2106.08011/) (2021)
- [Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approach](https://mlsanity.com/paper/2406.09776/) (2024)
- [Energy-Efficient Multi-Orchestrator Mobile Edge Learning](https://mlsanity.com/paper/2109.00757/) (2021)
- [Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approach](https://mlsanity.com/paper/2109.12409/) (2021)
- [Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updates](https://mlsanity.com/paper/2205.01470/) (2022)

## Abstract (arXiv)

Mobile edge learning is an emerging technique that enables distributed edge devices to collaborate in training shared machine learning models by exploiting their local data samples and communication and computation resources. To deal with the straggler dilemma issue faced in this technique, this paper proposes a new device to device enabled data sharing approach, in which different edge devices share their data samples among each other over communication links, in order to properly adjust their computation loads for increasing the training speed. Under this setup, we optimize the radio resource allocation for both data sharing and distributed training, with the objective of minimizing the total training delay under fixed numbers of local and global iterations. Numerical results show that the proposed data sharing design significantly reduces the training delay, and also enhances the training accuracy when the data samples are non independent and identically distributed among edge devices.