2 citations · 3 across the 5 of their papers we have counts for
5 papers
Decentralized Optimization with Amplified Privacy via Efficient Communication
Wei Huo, Changxin Liu, Kemi Ding +2
Decentralized optimization is crucial for multi-agent systems, with significant concerns about communication efficiency and privacy. This paper explores the role of efficient commu…
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…
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy
Wei Huo, Changxin Liu, Kemi Ding +2
This paper investigates the use of the cubic-regularized Newton method within a federated learning framework while addressing two major concerns that commonly arise in federated le…
Enhancing Privacy in Federated Learning through Local Training
Nicola Bastianello, Changxin Liu, Karl H. Johansson
In this paper we propose the federated learning algorithm Fed-PLT to overcome the challenges of (i) expensive communications and (ii) privacy preservation. We address (i) by allowi…
Differentially Private Set-Based Estimation Using Zonotopes
Mohammed M. Dawoud, Changxin Liu, Amr Alanwar +1
For large-scale cyber-physical systems, the collaboration of spatially distributed sensors is often needed to perform the state estimation process. Privacy concerns naturally arise…