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Found 14 papers, 2 papers with code
Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy
This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity, where the goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a central server.
Semi-Decentralized Federated Learning with Collaborative Relaying
We present a semi-decentralized federated learning algorithm wherein clients collaborate by relaying their neighbors' local updates to a central parameter server (PS).
Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying
Intermittent connectivity of clients to the parameter server (PS) is a major bottleneck in federated edge learning frameworks.
Minimax Optimal Quantization of Linear Models: Information-Theoretic Limits and Efficient Algorithms
We derive an information-theoretic lower bound for the minimax risk under this setting and propose a matching upper bound using randomized embedding-based algorithms which is tight up to constant factors.
Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks
The connectivity of each sensor cluster is intermittent and depends on the available communication opportunities of the sensors to the fusion center.
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication Budget
As a consequence, quantizing these embeddings followed by an inverse transform to the original space yields a source coding method with optimal covering efficiency while utilizing just $R$-bits per dimension.
Model-Based Machine Learning for Communications
We present an introduction to model-based machine learning for communication systems.
Interference Reduction in Virtual Cell Optimization
Our numerical results show that our scheme decreases the number of users in the system whose rate falls below the guaranteed rate, set to $128$kbps, $256$kbps or $512$kbps, when compared with our previously proposed optimization methods.
Learned Factor Graphs for Inference from Stationary Time Sequences
Learned factor graph can be realized using compact neural networks that are trainable using small training sets, or alternatively, be used to improve upon existing deep inference systems.
Data-Driven Symbol Detection via Model-Based Machine Learning
The design of symbol detectors in digital communication systems has traditionally relied on statistical channel models that describe the relation between the transmitted symbols and the observed signal at the receiver.
Data-Driven Factor Graphs for Deep Symbol Detection
In particular, we propose to use machine learning (ML) tools to learn the factor graph, instead of the overall system task, which in turn is used for inference by message passing over the learned graph.
Deep Neural Network Symbol Detection for Millimeter Wave Communications
This paper proposes to use a deep neural network (DNN)-based symbol detector for mmWave systems such that CSI acquisition can be bypassed.
ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection
Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the CSI-based Viterbi algorithm, and is capable of tracking time-varying channels without needing instantaneous CSI or additional training data.
Information Recovery from Pairwise Measurements
In particular, our results isolate a family of \emph{minimum} \emph{channel divergence measures} to characterize the degree of measurement corruption, which together with the size of the minimum cut of $\mathcal{G}$ dictates the feasibility of exact information recovery.
