# Coded Matrix Computations for D2D-enabled Linearized Federated Learning

## TL;DR

This paper introduces a novel coded matrix computation method for federated learning that reduces communication delays, enhances privacy, and improves local computation speed, especially with sparse data matrices.

## Contribution

It presents a straggler-optimal coded matrix computation approach tailored for D2D-enabled federated learning, addressing privacy and efficiency issues.

## Key findings

- Reduces communication delay in federated learning.
- Enhances privacy by minimizing D2D data transmissions.
- Improves local computation speed with sparse data matrices.

## Abstract

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.