12 citations · 23 across the 7 of their papers we have counts for
7 papers
Reputation-Based Byzantine-Resilient Vector Consensus without Prespecified Fault Bounds
Rui Huang, Changxin Liu, Wen-Hua Chen +1
This paper proposes an adaptive reputation-based algorithm for Byzantine-resilient vector consensus in distributed multi-agent systems. The proposed algorithm does not require a pr…
A survey on secure decentralized optimization and learning
Changxin Liu, Nicola Bastianello, Wei Huo +2
Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems and training large machine learning models without centralizing data. How…
Distributed Empirical Risk Minimization With Differential Privacy
Changxin Liu, Karl H. Johansson, Yang Shi
This work studies the distributed empirical risk minimization (ERM) problem under differential privacy (DP) constraint. Standard distributed algorithms achieve DP typically by pert…
Rate analysis of dual averaging for nonconvex distributed optimization
Changxin Liu, Xuyang Wu, Xinlei Yi +2
This work studies nonconvex distributed constrained optimization over stochastic communication networks. We revisit the distributed dual averaging algorithm, which is known to conv…
A Robust Distributed Model Predictive Control Framework for Consensus of Multi-Agent Systems with Input Constraints and Varying Delays
Henglai Wei, Changxin Liu, Yang Shi
This paper studies the consensus problem of general linear discrete-time multi-agent systems (MAS) with input constraints and bounded time-varying communication delays. We propose…
Achieving Model Fairness in Vertical Federated Learning
Changxin Liu, Zhenan Fan, Zirui Zhou +4
Vertical federated learning (VFL) has attracted greater and greater interest since it enables multiple parties possessing non-overlapping features to strengthen their machine learn…
Accelerated Dual Averaging Methods for Decentralized Constrained Optimization
Changxin Liu, Yang Shi, Huiping Li +1
In this work, we study decentralized convex constrained optimization problems in networks. We focus on the dual averaging-based algorithmic framework that is well-documented to be…