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Found 11 papers, 0 papers with code
Joint Transmit and Reflective Beamformer Design for Secure Estimation in IRS-Aided WSNs
Wireless sensor networks (WSNs) are vulnerable to eavesdropping as the sensor nodes (SNs) communicate over an open radio channel.
Decentralized Optimization with Distributed Features and Non-Smooth Objective Functions
We develop a new consensus-based distributed algorithm for solving learning problems with feature partitioning and non-smooth convex objective functions.
Cooperative Hybrid Transmit Beamforming in Cell-free mmWave MIMO Networks
Hybrid precoders and combiners are designed for cooperative cell-free multi-user millimeter wave (mmWave) multiple-input multiple-output (MIMO) cellular networks for low complexity interference mitigation.
Robust Linear Hybrid Beamforming Designs Relying on Imperfect CSI in mmWave MIMO IoT Networks
Linear hybrid beamformer designs are conceived for the decentralized estimation of a vector parameter in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) Internet of Things network (IoTNe).
Private Networked Federated Learning for Nonsmooth Objectives
This paper develops a networked federated learning algorithm to solve nonsmooth objective functions.
Hybrid Precoder and Combiner Designs for Decentralized Parameter Estimation in mmWave MIMO Wireless Sensor Networks
Hybrid precoder and combiner designs are conceived for decentralized parameter estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) wireless sensor networks (WSNs).
Distributed Filtering Design with Enhanced Resilience to Coordinated Byzantine Attacks
Numerical results demonstrate the accuracy of the proposed BR-CDF and its robustness against Byzantine attacks.
Robust Networked Federated Learning for Localization
This paper addresses the problem of localization, which is inherently non-convex and non-smooth in a federated setting where the data is distributed across a multitude of devices.
Moreau Envelope ADMM for Decentralized Weakly Convex Optimization
This paper proposes a proximal variant of the alternating direction method of multipliers (ADMM) for distributed optimization.
Smoothing ADMM for Sparse-Penalized Quantile Regression with Non-Convex Penalties
This paper investigates quantile regression in the presence of non-convex and non-smooth sparse penalties, such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD).
Distributed Maximum Consensus over Noisy Links
We introduce a distributed algorithm, termed noise-robust distributed maximum consensus (RD-MC), for estimating the maximum value within a multi-agent network in the presence of noisy communication links.
