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Found 10 papers, 1 papers with code
A survey on secure decentralized optimization and learning
Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems and training large machine learning models without centralizing data.
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy
This paper investigates the use of the cubic-regularized Newton method within a federated learning framework while addressing two major concerns that commonly arise in federated learning: privacy leakage and communication bottleneck.
Recent Advances in Data-driven Intelligent Control for Wireless Communication: A Comprehensive Survey
As the volume of data continues to escalate, the integration of data-driven methods has become indispensable for enabling adaptive and intelligent control mechanisms in future wireless communication systems.
Communication-efficient and Differentially-private Distributed Nash Equilibrium Seeking with Linear Convergence
Due to the nature of distributed systems, privacy and communication efficiency are two critical concerns.
Compression-based Privacy Preservation for Distributed Nash Equilibrium Seeking in Aggregative Games
To jointly address these issues, we propose an algorithm that uses stochastic compression to save communication resources and conceal information through random errors induced by compression.
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.
An Efficient Distributed Nash Equilibrium Seeking with Compressed and Event-triggered Communication
We demonstrate that SETC-DNES guarantees linear convergence to the NE while achieving even greater reductions in communication costs compared to ETC-DNES.
Distributed Nash Equilibrium Seeking with Stochastic Event-Triggered Mechanism
In this paper, we study the problem of consensus-based distributed Nash equilibrium (NE) seeking where a network of players, abstracted as a directed graph, aim to minimize their own local cost functions non-cooperatively.
Underwater Object Tracker: UOSTrack for Marine Organism Grasping of Underwater Vehicles
The UOHT training paradigm is designed to train the sample-imbalanced underwater tracker so that the tracker is exposed to a great number of underwater domain training samples and learns the feature expressions.
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.
