62 citations · 170 across the 44 of their papers we have counts for
3 papers · 1 filter
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data
Huancheng Chen, Haris Vikalo
Federated learning (FL) is a privacy-promoting framework that enables potentially large number of clients to collaboratively train machine learning models. In a FL system, a server…
Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints
Monica Ribero, Haris Vikalo, Gustavo De Veciana
Federated learning systems facilitate training of global models in settings where potentially heterogeneous data is distributed across a large number of clients. Such systems opera…
End-to-end system for object detection from sub-sampled radar data
Madhumitha Sakthi, Ahmed Tewfik, Marius Arvinte +1
Robust and accurate sensing is of critical importance for advancing autonomous automotive systems. The need to acquire situational awareness in complex urban conditions using senso…