| Paper ID | SPCOM-7.6 |
| Paper Title |
BYRDIE: BYZANTINE-RESILIENT DISTRIBUTED COORDINATE DESCENT FOR DECENTRALIZED LEARNING |
| Authors |
Zhixiong Yang, Rutgers University / Blue Danube, United States; Waheed Bajwa, Rutgers University, United States |
| Session | SPCOM-7: Communication-enabled Applications |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Signal Processing for Communications and Networking: [SPCN-DIST] Distributed, adaptive, and collaborative communication techniques |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfunctional occur frequently in the real world. This paper focuses on the problem of Byzantine failures, which are the hardest to safeguard against in distributed algorithms. While Byzantine fault tolerance has a rich history, existing work does not translate into efficient and practical algorithms for high-dimensional learning in fully distributed (also known as decentralized) settings. In this paper, an algorithm termed Byzantine-resilient distributed coordinate descent is developed and analyzed that enables distributed learning in the presence of Byzantine failures. Theoretical analysis (convex settings) and numerical experiments (convex and nonconvex settings) highlight its usefulness for high-dimensional distributed learning in the presence of Byzantine failures. |
