TY - GEN
T1 - Resilient Distributed Optimization
AU - Zhu, Jingxuan
AU - Lin, Yixuan
AU - Velasquez, Alvaro
AU - Liu, Ji
N1 - Publisher Copyright: © 2023 American Automatic Control Council.
PY - 2023
Y1 - 2023
N2 - This paper considers a distributed optimization problem in the presence of Byzantine agents capable of introducing untrustworthy information into the communication network. A resilient distributed subgradient algorithm is proposed based on graph redundancy and objective redundancy. It is shown that the algorithm causes all non-Byzantine agents' states to asymptotically converge to the same optimal point under appropriate assumptions. A partial convergence rate result is also provided.
AB - This paper considers a distributed optimization problem in the presence of Byzantine agents capable of introducing untrustworthy information into the communication network. A resilient distributed subgradient algorithm is proposed based on graph redundancy and objective redundancy. It is shown that the algorithm causes all non-Byzantine agents' states to asymptotically converge to the same optimal point under appropriate assumptions. A partial convergence rate result is also provided.
UR - https://www.scopus.com/pages/publications/85153722498
U2 - 10.23919/ACC55779.2023.10156564
DO - 10.23919/ACC55779.2023.10156564
M3 - Conference contribution
T3 - Proceedings of the American Control Conference
SP - 1307
EP - 1312
BT - 2023 American Control Conference, ACC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 American Control Conference, ACC 2023
Y2 - 31 May 2023 through 2 June 2023
ER -