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Found 9 papers, 2 papers with code
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification
Federated Learning (FL) methods adopt efficient communication technologies to distribute machine learning tasks across edge devices, reducing the overhead in terms of data storage and computational complexity compared to centralized solutions.
A physics-informed generative model for passive radio-frequency sensing
Electromagnetic (EM) body models predict the impact of human presence and motions on the Radio-Frequency (RF) stray radiation received by wireless devices nearby.
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning
The MTL process is carried out in two stages: the optimization of a meta-model that can be quickly adapted to learn new tasks, and a task-specific model adaptation stage where the learned meta-model is transferred to agents and tailored for a specific task.
An Energy and Carbon Footprint Analysis of Distributed and Federated Learning
Classical and centralized Artificial Intelligence (AI) methods require moving data from producers (sensors, machines) to energy hungry data centers, raising environmental concerns due to computational and communication resource demands, while violating privacy.
A Multisensory Edge-Cloud Platform for Opportunistic Radio Sensing in Cobot Environments
Worker monitoring and protection in collaborative robot (cobots) industrial environments requires advanced sensing capabilities and flexible solutions to monitor the movements of the operator in close proximity of moving robots.
A cloud-IoT platform for passive radio sensing: challenges and application case studies
Radio sensing and vision technologies allow to passively detect and track objects or persons by using radio waves as probe signals that encode a 2D/3D view of the environment they propagate through.
A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning
Recent advances in distributed learning raise environmental concerns due to the large energy needed to train and move data to/from data centers.
Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems
Next-generation autonomous and networked industrial systems (i. e., robots, vehicles, drones) have driven advances in ultra-reliable, low latency communications (URLLC) and computing.
Processing of body-induced thermal signatures for physical distancing and temperature screening
Massive and unobtrusive screening of people in public environments is becoming a critical task to guarantee safety in congested shared spaces, as well as to support early non-invasive diagnosis and response to disease outbreaks.
Human-Computer Interaction
