arXiv subjects

Mihitha Maithripala

Publications and source records attributed to Mihitha Maithripala.

4 recordsLinked to original sources

arXiv2026

A Momentum-based Stochastic Algorithm for Linearly Constrained Nonconvex Optimization

This paper studies a stochastic algorithm for linearly constrained nonconvex optimization, where the objective function is smooth but only unbiased stochastic gradients with bounded variance are available. We propose a momentum-based augmented Lagrangian method that employs a Polyak-type gradient estimator and requires only one stochastic gradient evaluation per iteration. Under the standard stochastic oracle model and the smoothness condition of the expected objective, we establish a convergence guarantee in terms of the first-order KKT residual of the original constrained problem. In particular, the proposed method computes an $\epsilon$-stationary solution in expectation within $O(\epsilon^{-4})$ stochastic gradient evaluations. Numerical experiments further show that the proposed method achieves competitive iteration complexity and improved wall-clock efficiency compared with representative recursive-momentum baselines.

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arXiv2026

Dynamic Average Consensus with Privacy Guarantees and Its Application to Battery Energy Storage Systems

A privacy-preserving dynamic average consensus (DAC) algorithm is proposed that achieves consensus while preventing external eavesdroppers from inferring the reference signals and their derivatives. During the initialization phase, each agent generates a set of sinusoidal signals with randomly selected frequencies and exchanges them with its neighboring agents to construct a masking signal. Each agent masks its reference signals using this composite masking signal before executing the DAC update rule. It is shown that the developed scheme preserves the convergence properties of the conventional DAC framework while preventing information leakage to external eavesdroppers. Furthermore, the developed algorithm is applied to state-of-charge (SoC) balancing in a networked battery energy storage system to demonstrate its practical applicability. Simulation results validate the theoretical findings.

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arXiv2026

Privacy-Preserving Dynamic Average Consensus by Masking Reference Signals

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. Because DAC requires exchanging state information with neighbors, attackers may gain access to these states and infer private information. In this paper, we develop a privacy-preserving method that protects each agent's reference signal from external eavesdroppers and honest-but-curious agents while achieving the same convergence accuracy and convergence rate as conventional DAC. Our approach masks the reference signals by having each agent draw a random real number for each neighbor, exchanges that number over an encrypted channel at the initialization, and computes a masking value to form a masked reference. Then the agents run the conventional DAC algorithm using the masked references. Convergence and privacy analyses show that the proposed algorithm matches the convergence properties of conventional DAC while preserving the privacy of the reference signals. Numerical simulations validate the effectiveness of the proposed privacy-preserving DAC algorithm.

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arXiv2025

Privacy-Preserving Distributed Control for a Networked Battery Energy Storage System

The increasing deployment of distributed Battery Energy Storage Systems (BESSs) in modern power grids necessitates effective coordination strategies to ensure state-of-charge (SoC) balancing and accurate power delivery. While distributed control frameworks offer scalability and resilience, they also raise significant privacy concerns due to the need for inter-agent information exchange. This paper presents a novel privacy-preserving distributed control algorithm for SoC balancing in a networked BESS. The proposed framework includes distributed power allocation law that is designed based on two privacy-preserving distributed estimators, one for the average unit state and the other for the average desired power. The average unit state estimator is designed via the state decomposition method without disclosing sensitive internal states. The proposed power allocation law based on these estimators ensures asymptotic SoC balancing and global power delivery while safeguarding agent privacy from external eavesdroppers. The effectiveness and privacy-preserving properties of the proposed control strategy are demonstrated through simulation results.

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