440 citations · 787 across the 29 of their papers we have counts for
8 papers · 1 filter
Bayesian Federated Learning for Continual Training
Usevalad Milasheuski, Luca Barbieri, Sanaz Kianoush +2
Bayesian Federated Learning (BFL) enables uncertainty quantification and robust adaptation in distributed learning. In contrast to the frequentist approach, it estimates the poster…
Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoT
Luca Barbieri, Stefano Savazzi, Monica Nicoli
Bayesian Federated Learning (FL) has been recently introduced to provide well-calibrated Machine Learning (ML) models quantifying the uncertainty of their predictions. Despite thei…
Cooperation and Federation in Distributed Radar Point Cloud Processing
S. Savazzi, V. Rampa, S. Kianoush +2
The paper considers the problem of human-scale RF sensing utilizing a network of resource-constrained MIMO radars with low range-azimuth resolution. The radars operate in the mmWav…
On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks
Usevalad Milasheuski, Luca Barbieri, Bernardo Camajori Tedeschini +2
Federated Learning (FL) allows multiple privacy-sensitive applications to leverage their dataset for a global model construction without any disclosure of the information. One of t…
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning
Stefano Savazzi, Vittorio Rampa, Sanaz Kianoush +1
Recent advances in Federated Learning (FL) have paved the way towards the design of novel strategies for solving multiple learning tasks simultaneously, by leveraging cooperation a…
An Energy and Carbon Footprint Analysis of Distributed and Federated Learning
Stefano Savazzi, Vittorio Rampa, Sanaz Kianoush +1
Classical and centralized Artificial Intelligence (AI) methods require moving data from producers (sensors, machines) to energy hungry data centers, raising environmental concerns…
A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning
Stefano Savazzi, Sanaz Kianoush, Vittorio Rampa +1
Recent advances in distributed learning raise environmental concerns due to the large energy needed to train and move data to/from data centers. Novel paradigms, such as federated…
Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems
Stefano Savazzi, Monica Nicoli, Mehdi Bennis +2
Next-generation autonomous and networked industrial systems (i.e., robots, vehicles, drones) have driven advances in ultra-reliable, low latency communications (URLLC) and computin…