6 papers
Improving Open-Set Semantic Segmentation in 3D Point Clouds by Conditional Channel Capacity Maximization: Preliminary Results
Wang Fang, Shirin Rahimi, Olivia Bennett +5
Point-cloud semantic segmentation underpins a wide range of critical applications. Although recent deep architectures and large-scale datasets have driven impressive closed-set per…
Over-the-Air Federated Learning in Satellite systems
Edward Akito Carlos, Raphael Pinard, Mitra Hassani
Federated learning in satellites offers several advantages. Firstly, it ensures data privacy and security, as sensitive data remains on the satellites and is not transmitted to a c…
Over-the-Air Federated Learning In Broadband Communication
Wayne Lemieux, Raphael Pinard, Mitra Hassani
Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that operates at the wireless edge. It enables clients to collaborate on model training while…
Federated Learning in MIMO Satellite Broadcast System
Raphael Pinard, Mitra Hassani, Wayne Lemieux
Federated learning (FL) is a type of distributed machine learning at the wireless edge that preserves the privacy of clients' data from adversaries and even the central server. Exi…
Resource Allocation in MIMO setup
Felix Ma Yun, Jordan Nabi, Mitra Hassani
In a multi-input multi-output (MIMO) setup, where one side of the link comprises a linear antenna array, data can be transmitted over the direction of incident rays. Channel capaci…
PHY-Fed: An Information-Theoretic Secure Aggregation in Federated Learning in Wireless Communications
Mitra Hassani, Reza Gholizadeh
Federated learning (FL) is a type of distributed machine learning at the wireless edge that preserves the privacy of clients' data from adversaries and even the central server. Exi…