Differentially Private Multi-Agent Constraint Optimization

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One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is ...

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...

Humanitarian aid-distribution programs help bring physical goods to people in need. Traditional paper-based solutions to support aid distribution do not scale to large populations and are hard to secure. Existing digital solutions solve these issues, at th ...

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...

This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances. A primal- ...

This paper presents a novel distributed approach for solving AC power flow (PF) problems. The optimization problem is reformulated into a distributed form using a communication structure corresponding to a hypergraph, by which complex relationships between ...

The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied ...

The integration of smart thermostats in home automation systems has created an opportunity to optimize space heating and cooling through the use of machine learning, for example for thermal model identification. Nonetheless, its full potential remains unta ...

Differentially Private Multi-Agent Constraint Optimization

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Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

Multi-agent Learning with Privacy Guarantees

Enforcing Privacy in Distributed Learning With Performance Guarantees

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Not Yet Another Digital ID: Privacy-Preserving Humanitarian Aid Distribution

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Privacy-preserving federated neural network training and inference

Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints

Hypergraph-Based Fast Distributed AC Power Flow Optimization

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Efficient and Accurate Peer-to-Peer Training of Machine Learning Based Home Thermal Models