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Found 7 papers, 3 papers with code
Energy Reduction in Cell-Free Massive MIMO through Fine-Grained Resource Management
Furthermore, we show that adopting a more distributed CF-mMIMO architecture is necessary to meet the data rate requirements.
Enhancing Privacy in Federated Learning through Local Training
In this paper we propose the federated private local training algorithm (Fed-PLT) for federated learning, to overcome the challenges of (i) expensive communications and (ii) privacy preservation.
Robust Online Learning over Networks
The recent deployment of multi-agent networks has enabled the distributed solution of learning problems, where agents cooperate to train a global model without sharing their local, private data.
Online Distributed Learning with Quantized Finite-Time Coordination
Online distributed learning refers to the process of training learning models on distributed data sources.
OpReg-Boost: Learning to Accelerate Online Algorithms with Operator Regression
This paper presents a new regularization approach -- termed OpReg-Boost -- to boost the convergence and lessen the asymptotic error of online optimization and learning algorithms.
tvopt: A Python Framework for Time-Varying Optimization
Then it discusses the different components of the framework and their use for modeling and solving time-varying optimization problems.
Extrapolation-based Prediction-Correction Methods for Time-varying Convex Optimization
In this paper, we focus on the solution of online optimization problems that arise often in signal processing and machine learning, in which we have access to streaming sources of data.
