Private and rateless adaptive coded matrix-vector multiplication

EURASIP Journal on Wireless Communications and Networking

Salim El Rouayheb¹  Rawad Bitar²  Hulya Seferoglu³  Yuxuan Xing³  Yasaman Keshtkarjahromi⁴  Venkat Dasari⁵ 

[1] Department of Electrical and Computer Engineering, Rutgers University;Department of Electrical and Computer Engineering, Technical University of Munich;Department of Electrical and Computer Engineering, University of Illinois at Chicago;Storage Research Group at the Seagate Technology;US Army Research Lab;

关键词: Distributed coded computing;    Secret sharing;    Rateless private codes;    Heterogeneous computing clusters;   

DOI  :  10.1186/s13638-020-01887-y

来源: DOAJ

【 摘 要 】

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 (1) the privacy requirements of IoT applications and devices, and (2) 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.

【 授权许可】

Unknown