5 papers
Efficient Federated Conformal Prediction with Group-Conditional Guarantee
Haifeng Wen, Osvaldo Simeone, Hong Xing
Deploying trustworthy AI systems requires principled uncertainty quantification. Conformal prediction (CP) is a widely used framework for constructing prediction sets with distribu…
Differential Privacy as a Perk: Federated Learning over Multiple-Access Fading Channels with a Multi-Antenna Base Station
Hao Liang, Haifeng Wen, Kaishun Wu +1
Federated Learning (FL) is a distributed learning paradigm that preserves privacy by eliminating the need to exchange raw data during training. In its prototypical edge instantiati…
Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs
Haifeng Wen, Hong Xing, Osvaldo Simeone
For modern artificial intelligence (AI) applications such as large language models (LLMs), the training paradigm has recently shifted to pre-training followed by fine-tuning. Furth…
Distributed Conformal Prediction via Message Passing
Haifeng Wen, Hong Xing, Osvaldo Simeone
Post-hoc calibration of pre-trained models is critical for ensuring reliable inference, especially in safety-critical domains such as healthcare. Conformal Prediction (CP) offers a…
NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection
Haifeng Wen, Nicolò Michelusi, Osvaldo Simeone +1
Over-the-air federated learning (FL), i.e., AirFL, leverages computing primitively over multiple access channels. A long-standing challenge in AirFL is to achieve coherent signal a…