TiBroco: A Fast and Secure Distributed Learning Framework for Tiered Wireless Edge Networks
Dong-Jun Han, Jy-yong Sohn, Jaekyun J. Moon · 2021
Recent proliferation of mobile devices and edge servers (e.g., small base stations) strongly motivates distributed learning at the wireless edge. In this paper, we propose a fast and secure distributed learning framework that utilizes computing resources at edge servers as well as distributed computing devices in tiered wireless edge networks. A fundamental lower bound is derived on the computational load that perfectly tolerates Byzantine attacks at both tiers. TiBroco, a hierarchical coding framework achieving this theoretically minimum computational load is proposed, which guarantees secure distributed learning by combating Byzantines. A fast distributed learning is possible by precisely allocating loads to the computing devices and edge servers, and also utilizing the broadcast nature of wireless devices. Extensive experimental results on Amazon EC2 indicate that our TiBroco allows significantly faster distributed learning than existing methods while guaranteeing full tolerance against Byzantine attacks at both tiers.