Securely Aggregated Coded Matrix Inversion

TL;DR

This paper introduces a secure, coded matrix inversion method for decentralized networks that is resilient to stragglers and preserves data privacy, applicable to federated learning scenarios with secure communication.

Contribution

It proposes a novel coded computing approach based on gradient coding that ensures data privacy and security against eavesdroppers in a decentralized setting.

Findings

Method effectively mitigates stragglers in matrix inversion tasks.

Ensures data privacy by preventing access to local data at the coordinator.

Network communications are secure against eavesdroppers.

Abstract

Coded computing is a method for mitigating straggling workers in a centralized computing network, by using erasure-coding techniques. Federated learning is a decentralized model for training data distributed across client devices. In this work we propose approximating the inverse of an aggregated data matrix, where the data is generated by clients; similar to the federated learning paradigm, while also being resilient to stragglers. To do so, we propose a coded computing method based on gradient coding. We modify this method so that the coordinator does not access the local data at any point; while the clients access the aggregated matrix in order to complete their tasks. The network we consider is not centrally administrated, and the communications which take place are secure against potential eavesdroppers.