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Found 6 papers, 1 papers with code
VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning
In this domain, federated learning is one of the most effective and promising techniques for training global machine learning models, while preserving data privacy at the vehicles and optimizing communications resource usage.
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization
Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms.
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling
Federated learning (FL) has recently gained much attention due to its effectiveness in speeding up supervised learning tasks under communication and privacy constraints.
A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels
In the last years, several machine learning-based techniques have been proposed to monitor human movements from Wi-Fi channel readings.
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing
There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL).
SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points
SHARP is trained on data collected as a person performs seven different activities in a single environment.
