4 papers · 1 filter
Communication-Efficient Differentially Private Gradient Tracking for Distributed Optimization via Local Updates
Mihitha Maithripala, Chenyang Qiu, Zongli Lin
This paper studies privacy-preserving distributed optimization using gradient tracking with one communication-free local update between consecutive communication rounds. Since loca…
A Momentum-based Stochastic Algorithm for Linearly Constrained Nonconvex Optimization
Chenyang Qiu, Mihitha Maithripala, Zongli Lin
This paper studies a stochastic algorithm for linearly constrained nonconvex optimization, where the objective function is smooth but only unbiased stochastic gradients with bounde…
Dynamic Average Consensus with Privacy Guarantees and Its Application to Battery Energy Storage Systems
Mihitha Maithripala, Chenyang Qiu, Zongli Lin
A privacy-preserving dynamic average consensus (DAC) algorithm is proposed that achieves consensus while preventing external eavesdroppers from inferring the reference signals and…
Privacy-Preserving Dynamic Average Consensus by Masking Reference Signals
Mihitha Maithripala, Zongli Lin
In multi-agent systems, dynamic average consensus (DAC) is a decentralized estimation strategy in which a set of agents tracks the average of time-varying reference signals. Becaus…