2019

BRIDGE: Byzantine-resilient Decentralized Gradient Descent

Fang, Cheng, Yang, Zhixiong, Bajwa, Waheed U.

Understand

Machine learning has begun to play a central role in many applications.

  • A multitude of these applications typically also involve datasets that are distributed across multiple computing devices/machines due to either design constraints (e.g., multiagent systems) or computational/privacy reasons (e.g., learning on smartphone data).
  • Such applications often require the learning tasks to be carried out in a decentralized fashion, in which there is no central server that is directly connected to all nodes.
  • In real-world decentralized settings, nodes are prone to undetected failures due to malfunctioning equipment, cyberattacks, etc., which are likely to crash non-robust learning algorithms.

Reading the bibliography…