8 papers
Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
Seyed Mohammad Azimi-Abarghouyi, Carlo Fischione, Kaibin Huang
Over-the-Air Federated Learning (AirFL) is an emerging paradigm that tightly integrates wireless signal processing and distributed machine learning to enable scalable AI at the net…
DP2Guard: A Lightweight and Byzantine-Robust Privacy-Preserving Federated Learning Scheme for Industrial IoT
Baofu Han, Bing Li, Yining Qi +4
Privacy-Preserving Federated Learning (PPFL) has emerged as a secure distributed Machine Learning (ML) paradigm that aggregates locally trained gradients without exposing raw data.…
Large Speech Model Enabled Semantic Communication
Yun Tian, Zhijin Qin, Guocheng Lv +3
Existing speech semantic communication systems mainly based on Joint Source-Channel Coding (JSCC) architectures have demonstrated impressive performance, but their effectiveness re…
Waveforms for Computing Over the Air
Ana Pérez-Neira, Marc Martinez-Gost, Alphan Åahin +3
Over-the-air computation (AirComp) leverages the signal-superposition characteristic of wireless multiple access channels to perform mathematical computations. Initially introduced…
Detection Capability Comparison Between Intensity Detection and Splitting Detection for Rydberg-Atomic Sensors
Hao Wu, Xinyuan Yao, Rui Ni +2
Rydberg atomic quantum receivers have been seen as novel radio frequency measurements and the high sensitivity to a large range of frequencies makes it attractive for communication…
Fault-Free Analog Computing with Imperfect Hardware
Zhicheng Xu, Jiawei Liu, Sitao Huang +9
The growing demand for edge computing and AI drives research into analog in-memory computing using memristors, which overcome data movement bottlenecks by computing directly within…