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Found 16 papers, 0 papers with code
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.
Policy Gradient-based Model Free Optimal LQG Control with a Probabilistic Risk Constraint
In this paper, we investigate a model-free optimal control design that minimizes an infinite horizon average expected quadratic cost of states and control actions subject to a probabilistic risk or chance constraint using input-output data.
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.
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.
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.
FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation
Federated learning is a distributed learning framework that allows a set of clients to collaboratively train a model under the orchestration of a central server, without sharing raw data samples.
Deterministic policy gradient based optimal control with probabilistic constraints
Two strategies are employed to manage the probabilistic constraint in scenarios of known and unknown system models.
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization
Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms.
Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus
This paper considers the problem of distributed multi-agent learning, where the global aim is to minimize a sum of local objective (empirical loss) functions through local optimization and information exchange between neighbouring nodes.
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.
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing
There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL).
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.
Transmission Power Allocation for Remote Estimation with Multi-packet Reception Capabilities
In this paper we consider the problem of transmission power allocation for remote estimation of a dynamical system in the case where the estimator is able to simultaneously receive packets from multiple interfering sensors, as it is possible e. g. with the latest wireless technologies such as 5G and WiFi.
Quickest Detection of Deception Attacks in Networked Control Systems with Physical Watermarking
An independent and identically distributed watermarking signal is added to the optimal linear quadratic Gaussian (LQG) control inputs, and a cumulative sum (CUSUM) test is carried out using the joint distribution of the innovation signal and the watermarking signal for quickest attack detection.
Optimization and Control
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.
