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PhD Forum Abstract: Efficient Computing and Communication Paradigms for Federated Learning Data Streams

Artificial intelligence of things at the edge: Scalable and efficient distributed learning for massive scenarios

Federated Learning with Time-based Client Selection to Optimize Edge Resources

Drivers Stress Identification in Real-World Driving Tasks

Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks

A Novel Approach to Distributed Model Aggregation using Apache Kafka

AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving

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OFFERTA FORMATIVA A.A. 2024-2025

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