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Found 19 papers, 2 papers with code
Analysis of a Reduced-Communication Diffusion LMS Algorithm
In diffusion-based algorithms for adaptive distributed estimation, each node of an adaptive network estimates a target parameter vector by creating an intermediate estimate and then combining the intermediate estimates available within its closed neighborhood.
Recursive Total Least-Squares Algorithm Based on Inverse Power Method and Dichotomous Coordinate-Descent Iterations
We develop a recursive total least-squares (RTLS) algorithm for errors-in-variables system identification utilizing the inverse power method and the dichotomous coordinate-descent (DCD) iterations.
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis
In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
On Stability and Convergence of Distributed Filters
The paradigm of stability and convergence in distributed filtering is revised in this manuscript.
Communication-Efficient Online Federated Learning Framework for Nonlinear Regression
As a solution, this paper presents a partial-sharing-based online federated learning framework (PSO-Fed) that enables clients to update their local models using continuous streaming data and share only portions of those updated models with the server.
Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression
In this manner, we reduce the communication load of the participants and, therefore, render participation in the learning task more accessible.
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.
Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs
The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs).
Asynchronous Online Federated Learning with Reduced Communication Requirements
The simulations reveal that in asynchronous settings, the proposed PAO-Fed is able to achieve the same convergence properties as that of the online federated stochastic gradient while reducing the communication overhead by 98 percent.
Personalized Graph Federated Learning with Differential Privacy
Further, our analysis shows that the algorithm ensures local differential privacy for all clients in terms of zero-concentrated differential privacy.
Private Networked Federated Learning for Nonsmooth Objectives
This paper develops a networked federated learning algorithm to solve nonsmooth objective functions.
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).
Analyzing the Impact of Partial Sharing on the Resilience of Online Federated Learning Against Model Poisoning Attacks
PSO-Fed reduces the communication load by enabling clients to exchange only a fraction of their model estimates with the server at each update round.
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.
Privacy-Preserving Distributed Nonnegative Matrix Factorization
To address this, we propose a privacy-preserving algorithm for fully-distributed NMF that decomposes a distributed large data matrix into left and right matrix factors while safeguarding each agent's local data privacy.
Perceptual and acoustic analysis of voice similarities between parents and young children
Human voice provides the means for verbal communication and forms a part of personal identity.
