Distributed Differentially Private Control Synthesis for Multi-Agent Systems with Metric Temporal Logic Specifications
In this paper, we propose a distributed differentially private receding horizon control (RHC) approach for multi-agent systems (MAS) with metric temporal logic (MTL) specifications. In the MAS considered in this paper, each agent privatizes its sensitive information from other agents using a differential privacy mechanism. In other words, each agent adds privacy noise (e.g., Gaussian noise) to its output to maintain its privacy and communicates its noisy output with its neighboring agents. We define two types of MTL specifications for the MAS: agent-level specifications and system-level specifications. Agents should collaborate to satisfy the system-level MTL specifications with a minimum probability while each agent must satisfy its own agent-level MTL specifications at the same time. In the proposed distributed RHC approach, each agent communicates with its neighboring agents to acquire their noisy outputs and calculates an estimate of the system-level trajectory. Then each agent synthesizes its own control inputs such that the system-level specifications are satisfied with a minimum probability while the agent-level specifications are also satisfied. In the proposed optimization formulation of RHC, we directly incorporate Kalman filter equations to calculate the estimates of the system-level trajectory, and we use mixed-integer linear programming (MILP) to encode the MTL specifications as optimization constraints. Finally, we implement the proposed distributed RHC approach in a case study.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Differentially Private ADMM for Distributed Medical Machine Learning
Due to massive amounts of data distributed across multiple locations, distributed machine learning has attracted a lot of research interests. Alternating Direction Method of Multipliers (ADMM) is a powerful method of des…
BIG-bench Machine LearningDifferentially Private Tabular Data Synthesis using Large Language Models
Synthetic tabular data generation with differential privacy is a crucial problem to enable data sharing with formal privacy. Despite a rich history of methodological research and development, developing differentially pr…
FairnessTabular Data GenerationDon’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Although machine learning models trained on massive data have led to breakthroughs in several areas, their deployment in privacy-sensitive domains remains limited due to restricted access to data. Generative models train…
DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase
Differentially private synthetic data is a promising alternative for sensitive data release. Many differentially private generative models have been proposed in the literature. Unfortunately, they all suffer from the low…
Image GenerationDon't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Although machine learning models trained on massive data have led to break-throughs in several areas, their deployment in privacy-sensitive domains remains limited due to restricted access to data. Generative models trai…