6 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 +2
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…
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…
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…
AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory
Haifeng Wen, Hong Xing, Osvaldo Simeone
Addressing the communication bottleneck inherent in federated learning (FL), over-the-air FL (AirFL) has emerged as a promising solution, which is, however, hampered by deep fading…