The integration of Artificial Intelligence (AI) into wireless systems is reshaping how we design, optimize, and operate communication networks.
This seminar presents a short overview of emerging AI-driven paradigms at the wireless edge, with emphasis on distributed learning frameworks such as federated learning, and the associated challenges of communication-efficiency, privacy, and model heterogeneity. We will explore how Edge AI leverages local computation and collaborative training to support intelligent services under strict latency and bandwidth constraints. Special attention will be given to Digital Over-the-Air Computation as a growndbreaking physical-layer technique enabling fast and scalable model aggregation directly over wireless channels. Throughout the talk, we will connect these developments to fundamental questions in optimization, generalization, and system co-design.
The seminar aims to provide a research-oriented perspective on AI for next-generation wireless networks.
The integration of Artificial Intelligence (AI) into wireless systems is reshaping how we design, optimize, and operate communication networks.
This seminar presents a short overview of emerging AI-driven paradigms at the wireless edge, with emphasis on distributed learning frameworks such as federated learning, and the associated challenges of communication-efficiency, privacy, and model heterogeneity. We will explore how Edge AI leverages local computation and collaborative training to support intelligent services under strict latency and bandwidth constraints. Special attention will be given to Digital Over-the-Air Computation as a growndbreaking physical-layer technique enabling fast and scalable model aggregation directly over wireless channels. Throughout the talk, we will connect these developments to fundamental questions in optimization, generalization, and system co-design.
The seminar aims to provide a research-oriented perspective on AI for next-generation wireless networks.