# Coded Matrix Computations for D2D-enabled Linearized Federated Learning

arXiv: https://arxiv.org/abs/2302.12305 · HTML: https://mlsanity.com/paper/2302.12305/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [Speeding Up Distributed Machine Learning Using Codes](https://mlsanity.com/paper/1512.02673/) (2015)
- [Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation](https://mlsanity.com/paper/2101.00787/) (2021)
- [Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data](https://mlsanity.com/paper/1905.07210/) (2019)
- [Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication](https://mlsanity.com/paper/1705.10464/) (2017)
- [Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks](https://mlsanity.com/paper/2011.06223/) (2020)
- [On the Optimal Recovery Threshold of Coded Matrix Multiplication](https://mlsanity.com/paper/1801.10292/) (2018)
- [Network-Aware Optimization of Distributed Learning for Fog Computing](https://mlsanity.com/paper/2004.08488/) (2020)
- [UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach](https://mlsanity.com/paper/2106.15734/) (2021)
- [Coded Federated Learning](https://mlsanity.com/paper/2002.09574/) (2020)
- [Coded sparse matrix computation schemes that leverage partial stragglers](https://mlsanity.com/paper/2012.06065/) (2020)
- [Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange](https://mlsanity.com/paper/2208.02856/) (2022)

## Abstract (arXiv)

Federated learning (FL) is a popular technique for training a global model on data distributed across client devices. Like other distributed training techniques, FL is susceptible to straggler (slower or failed) clients. Recent work has proposed to address this through device-to-device (D2D) offloading, which introduces privacy concerns. In this paper, we propose a novel straggler-optimal approach for coded matrix computations which can significantly reduce the communication delay and privacy issues introduced from D2D data transmissions in FL. Moreover, our proposed approach leads to a considerable improvement of the local computation speed when the generated data matrix is sparse. Numerical evaluations confirm the superiority of our proposed method over baseline approaches.