Zheng Chang

Communication-Efficient Federated Learning in Channel Constrained Internet of Things

2022 · GLOBECOM 2022 - 2022 IEEE Global Communications Conference

Federated learning (FL) is able to utilize the computing capability and maintain the privacy of the end devices by collecting and aggregating the locally trained learning model parameters while keeping the local personal data. As …

Fed2VAEs: An Efficient Privacy-Preserving Federated Learning Approach Based on Variational Autoencoders

2024

Recently, federated learning (FL) has been threat-ened by the gradient inversion attack that infers user-private data from shared gradients. To cope with this problem, the differential privacy (DP) technique is widely employed in FL. However, …

GenFV: AIGC-Assisted Federated Learning for Vehicular Edge Intelligence

2025

Federated Learning (FL) has emerged as a promising technology for privacy-preserving vehicular applications. However, its performance is frequently limited by challenges such as vehicle mobility, unstable wireless channels, and heterogeneous data distributions. To address these …