803 citations · 3.5k across the 244 of their papers we have counts for
147 papers · 1 filter
Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction
Meiyi Zhu, Matteo Zecchin, Sangwoo Park +3
In this paper, we consider a wireless federated inference scenario in which devices and a server share a pre-trained machine learning model. The devices communicate statistical inf…
Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clipping
Haifeng Wen, Hong Xing, Osvaldo Simeone
One of the key challenges towards the deployment of over-the-air federated learning (AirFL) is the design of mechanisms that can comply with the power and bandwidth constraints of…
Adaptive and Flexible Model-Based AI for Deep Receivers in Dynamic Channels
Tomer Raviv, Sangwoo Park, Osvaldo Simeone +2
Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn to operate in ch…
Bayesian Over-the-Air FedAvg via Channel Driven Stochastic Gradient Langevin Dynamics
Boning Zhang, Dongzhu Liu, Osvaldo Simeone +1
The recent development of scalable Bayesian inference methods has renewed interest in the adoption of Bayesian learning as an alternative to conventional frequentist learning that…
Uncertainty-Aware and Reliable Neural MIMO Receivers via Modular Bayesian Deep Learning
Tomer Raviv, Sangwoo Park, Osvaldo Simeone +1
Deep learning is envisioned to play a key role in the design of future wireless receivers. A popular approach to design learning-aided receivers combines deep neural networks (DNNs…
Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference
Jiechen Chen, Nicolas Skatchkovsky, Osvaldo Simeone
Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips,…