Private and Rateless Adaptive Coded Matrix-Vector Multiplication

TL;DR

This paper introduces PRAC, a private and adaptive coded computation algorithm for matrix-vector multiplication at the edge, addressing privacy and resource heterogeneity in IoT environments.

Contribution

It presents a novel rateless adaptive coded computation method that ensures privacy and efficiently handles heterogeneous, time-varying edge device resources.

Findings

PRAC outperforms existing secure coded computing methods in heterogeneous settings.

Theoretical guarantees demonstrate PRAC's effectiveness and efficiency.

Simulations and Android implementations validate the practical benefits of PRAC.

Abstract

Edge computing is emerging as a new paradigm to allow processing data near the edge of the network, where the data is typically generated and collected. This enables critical computations at the edge in applications such as Internet of Things (IoT), in which an increasing number of devices (sensors, cameras, health monitoring devices, etc.) collect data that needs to be processed through computationally intensive algorithms with stringent reliability, security and latency constraints. Our key tool is the theory of coded computation, which advocates mixing data in computationally intensive tasks by employing erasure codes and offloading these tasks to other devices for computation. Coded computation is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication costs. In this paper, we develop a private and rateless adaptive coded computation (PRAC) algorithm for distributed matrix-vector multiplication by taking into account (i) the privacy requirements of IoT applications and devices, and (ii) the heterogeneous and time-varying resources of edge devices. We show that PRAC outperforms known secure coded computing methods when resources are heterogeneous. We provide theoretical guarantees on the performance of PRAC and its comparison to baselines. Moreover, we confirm our theoretical results through simulations and implementations on Android-based smartphones.