3 citations · 8 across the 9 of their papers we have counts for
9 papers
Achieving Linear Speedup for Composite Federated Learning
Kun Huang, Shi Pu, Karl Henrik Johansson
This paper proposes FedNMap, a normal map-based method for composite federated learning, where the objective consists of a smooth loss and a possibly nonsmooth regularizer. FedNMap…
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…
Byzantine-Robust and Communication-Efficient Distributed Learning via Compressed Momentum Filtering
Changxin Liu, Yanghao Li, Yuhao Yi +1
Distributed learning has become the standard approach for training large-scale machine learning models across private data silos. While distributed learning enhances privacy preser…
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…
Achieving violation-free distributed optimization under coupling constraints
Changxin Liu, Xiao Tan, Xuyang Wu +2
Constraint satisfaction is a critical component in a wide range of engineering applications, including but not limited to safe multi-agent control and economic dispatch in power sy…
A continuous-time violation-free multi-agent optimization algorithm and its applications to safe distributed control
Xiao Tan, Changxin Liu, Karl H. Johansson +1
In this work, we propose a continuous-time distributed optimization algorithm with guaranteed zero coupling constraint violation and apply it to safe distributed control in the pre…
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…
synchronization of higher-order nonlinear systems with a recursive Lyapunov approach
Davide Liuzza, Dimos V. Dimarogonas, Karl H. Johansson
This paper investigates the problem of synchronization for nonlinear systems. Following a Lyapunov approach, we firstly study global synchronization of nonlinear systems in canonic…