10 accepted papers
In decentralized systems like the Internet of Things (IoT) and cyber-physical networks, where data are distributed across multiple nodes, ensuring accurate and robust data analysis is crucial. Existing methods for penalized quantile regression often struggle with asynchronous operations and multiple…
We investigate the robustness of the recently introduced partialsharing online federated learning (PSO-Fed) algorithm against model-poisoning attacks. To this end, we analyze the performance of the PSO-Fed algorithm in the presence of Byzantine clients, who may clandestinely corrupt their local mode…
Federated learning (FL) literature typically assumes that each client has a fixed amount of data, which is unrealistic in many practical applications. Some recent works introduced a framework for online FL (Online-Fed) wherein clients perform model learning on streaming data and communicate the mode…
This work proposes an efficient batch-based implementation for kernel regression on graphs (KRG) using random Fourier features (RFF) and a low-complexity online implementation. Kernel regression has proven to be an efficient learning tool in the graph signal processing framework. However, it suffers…
We propose a new distributed algorithm to solve the total least-squares (TLS) problem when data are distributed over a multi-agent network. To develop the proposed algorithm, named distributed ADMM TLS (DA-TLS), we reformulate the TLS problem as a parametric semidefinite program and solve it using t…
In this paper we propose a genie-aided strategy to optimize the use of renewable energy (RE) in a community of households with shared access to storage and RE generation facilities. The households are spread over a limited geographical area, and are subject to different time-varying power consumptio…
In order to accommodate for modern adaptive filtering applications, the classic adaptive filtering paradigm is considered from a more general perspective. The new formulation allows for time dependent variations in the state of the system and more importantly it relaxes the Gaussian assumption to th…
In this work, a distributed Kalman filtering and clustering framework for sensor networks tasked with tracking multiple state vector sequences is developed. This is achieved through recursively updating the likelihood of a state vector estimation from one agent offering valid information about the s…
We develop a fully-distributed iterative algorithm for finding a model-distributed least-squares solution of systems of linear equations over sensor networks. Here, model-distributed means the solution vector is distributed across the network rather than being replicated at each node. For this purpo…
In this paper, we consider the problem of channel and phase noise estimation for an orthogonal frequency division multiplexing (OFDM) radio link. We solve this problem by first investigating the subspace in which the phase noise spectral vector lies and then exploiting this information during estima…