Ning Wang

Ning Wang

Assistant Professor at USF

Talks and presentations

Flare: Defensing Federated Learning against Model Poisoning Attacks via Latent Space Representations

June 02, 2022

Talk, AsiaCCS 2022, Recorded, the talk is shared at the conference in Nagasaki, Japan

This talk presents a robust aggregation algorithm FLARE to protect FL against MPAs. It demonstrates that PLR vector has high potentials in differentiating malicious/poisonous models from the benign ones. FLARE effectively minimizes the impact of malicious/poisonous models on the final aggregation by assigning low trust scores to those with diverging PLRs.

FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning

May 05, 2022

Conference proceedings talk, IEEE INFOCOM 2022, Recorded Talk for Virtual Conference

This talk presents FeCo, a machine-learning-based IDS for IoT networks. FeCo incorporates contrastive learning into FL framework to support distributed intrusion detection. FeCo obtains more representative feature vectors by contrastive learning. These feature vectors have higher discriminative power between normal and malicious traffic. This effectively enables FeCo to achieve better detection accuracy than other baselines. Through extensive evaluations on the NSL-KDD dataset, we demonstrate the high effectiveness of FeCo in both centralized and federated learning setting.