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Found 10 papers, 1 papers with code
$S^2$NeRF: Privacy-preserving Training Framework for NeRF
Neural Radiance Fields (NeRF) have revolutionized 3D computer vision and graphics, facilitating novel view synthesis and influencing sectors like extended reality and e-commerce.
Differentially Private Dual Gradient Tracking for Distributed Resource Allocation
This paper investigates privacy issues in distributed resource allocation over directed networks, where each agent holds a private cost function and optimizes its decision subject to a global coupling constraint through local interaction with other agents.
Distributed Average Consensus via Noisy and Non-Coherent Over-the-Air Aggregation
Over-the-air aggregation has attracted widespread attention for its potential advantages in task-oriented applications, such as distributed sensing, learning, and consensus.
Over-the-air Federated Policy Gradient
In recent years, over-the-air aggregation has been widely considered in large-scale distributed learning, optimization, and sensing.
Sensor Selection for Remote State Estimation with QoS Requirement Constraints
By utilizing the solution of the relaxed problem, we propose a heuristic sensor selection algorithm which can provide a good suboptimal solution.
LQG Control Over SWIPT-enabled Wireless Communication Network
In this paper, we consider using simultaneous wireless information and power transfer (SWIPT) to recharge the sensor in the LQG control, which provides a new approach to prolonging the network lifetime.
Sensor Scheduling Design for Complex Networks under a Distributed State Estimation Framework
The goal of this paper is to seek an optimal sensor scheduling policy minimizing the overall estimation errors.
Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trends
In order to improve the speed of B&B algorithms, learning techniques have been introduced in this algorithm recently.
A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition
For privacy preservation, we propose a novel state-decomposition based gradient tracking approach (SD-Push-Pull) for distributed optimzation over directed networks that preserves differential privacy, which is a strong notion that protects agents' privacy against an adversary with arbitrary auxiliary information.
Privacy-Preserving Push-sum Average Consensus via State Decomposition
That is to say, only the exchanged substate would be visible to an adversary, preventing the initial state information from leakage.
