18 citations · 19 across the 6 of their papers we have counts for
6 papers
Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives
Jiaojiao Zhang, Dominik Fay, Mikael Johansson
This paper proposes a locally differentially private federated learning algorithm for strongly convex but possibly nonsmooth problems that protects the gradients of each worker aga…
Achieving Consensus over Compact Submanifolds
Jiang Hu, Jiaojiao Zhang, Kangkang Deng
We consider the consensus problem in a decentralized network, focusing on a compact submanifold that acts as a nonconvex constraint set. By leveraging the proximal smoothness of th…
A Communication-Efficient Decentralized Newton's Method with Provably Faster Convergence
Huikang Liu, Jiaojiao Zhang, Anthony Man-Cho So +1
In this paper, we consider a strongly convex finite-sum minimization problem over a decentralized network and propose a communication-efficient decentralized Newton's method for so…
Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning: Part II
Jiaojiao Zhang, Huikang Liu, Anthony Man-Cho So +1
In Part I of this work, we have proposed a general framework of decentralized stochastic quasi-Newton methods, which converge linearly to the optimal solution under the assumption…
Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning: Part I
Jiaojiao Zhang, Huikang Liu, Anthony Man-Cho So +1
In this work, we investigate stochastic quasi-Newton methods for minimizing a finite sum of cost functions over a decentralized network. In Part I, we develop a general algorithmic…
A Newton Tracking Algorithm with Exact Linear Convergence Rate for Decentralized Consensus Optimization
Jiaojiao Zhang, Qing Ling, Anthony Man-Cho So
This paper considers the decentralized consensus optimization problem defined over a network where each node holds a second-order differentiable local objective function. Our goal…