Mitra Hassani
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View article: Improving Open-Set Semantic Segmentation in 3D Point Clouds by Conditional Channel Capacity Maximization: Preliminary Results
Improving Open-Set Semantic Segmentation in 3D Point Clouds by Conditional Channel Capacity Maximization: Preliminary Results Open
Point-cloud semantic segmentation underpins a wide range of critical applications. Although recent deep architectures and large-scale datasets have driven impressive closed-set performance, these models struggle to recognize or properly se…
View article: Over-the-Air Federated Learning in Satellite systems
Over-the-Air Federated Learning in Satellite systems Open
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 central location. This is particularly important when dealin…
View article: Over-the-Air Federated Learning In Broadband Communication
Over-the-Air Federated Learning In Broadband Communication Open
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 keeping their data private from adversaries and the central…
View article: Federated Learning in MIMO Satellite Broadcast System
Federated Learning in MIMO Satellite Broadcast System Open
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. Existing federated learning approaches either use (i) secure m…
View article: Resource Allocation in MIMO setup
Resource Allocation in MIMO setup Open
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 capacity for this setup is studied in this paper. We define two d…
View article: PHY-Fed: An Information-Theoretic Secure Aggregation in Federated Learning in Wireless Communications
PHY-Fed: An Information-Theoretic Secure Aggregation in Federated Learning in Wireless Communications Open
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. Existing federated learning approaches either use (i) secure m…