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Found 11 papers, 2 papers with code
Energy-Aware Federated Learning in Satellite Constellations
Federated learning in satellite constellations, where the satellites collaboratively train a machine learning model, is a promising technology towards enabling globally connected intelligence and the integration of space networks into terrestrial mobile networks.
Sparse Incremental Aggregation in Multi-Hop Federated Learning
This paper investigates federated learning (FL) in a multi-hop communication setup, such as in constellations with inter-satellite links.
Scheduling for On-Board Federated Learning with Satellite Clusters
Mega-constellations of small satellites have evolved into a source of massive amount of valuable data.
Robust Precoding via Characteristic Functions for VSAT to Multi-Satellite Uplink Transmission
In case of imperfect position knowledge, the performance degradation of the robust precoder is relatively small.
Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellations
Distributed training of machine learning models directly on satellites in low Earth orbit (LEO) is considered.
Federated Learning in Satellite Constellations
Federated learning (FL) has recently emerged as a distributed machine learning paradigm for systems with limited and intermittent connectivity.
Beamspace MIMO for Satellite Swarms
In this paper, we propose a distributed linear precoding scheme and a GS equalizer relying on local position information.
On-Board Federated Learning for Dense LEO Constellations
Mega-constellations of small-size Low Earth Orbit (LEO) satellites are currently planned and deployed by various private and public entities.
Ground-Assisted Federated Learning in LEO Satellite Constellations
In Low Earth Orbit (LEO) mega constellations, there are relevant use cases, such as inference based on satellite imaging, in which a large number of satellites collaboratively train a machine learning model without sharing their local datasets.
Reconfigurable Intelligent Surfaces: A Signal Processing Perspective With Wireless Applications
We will provide the formulas and derivations that are required to understand and analyze RIS-aided systems using signal processing, and exemplify how they can be utilized for improved communication, localization, and sensing.
A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks
Specifically, thanks to its reduced complexity, the proposed method can be used to train an artificial neural network to predict the optimal resource allocation.
