AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems

no code implementations • 22 Nov 2023 • Chentao Jia, Ming Hu, Zekai Chen, Yanxin Yang, Xiaofei Xie, Yang Liu, Mingsong Chen

Although Federated Learning (FL) is promising to enable collaborative learning among Artificial Intelligence of Things (AIoT) devices, it suffers from the problem of low classification performance due to various heterogeneity factors (e. g., computing capacity, memory size) of devices and uncertain operating environments.

Federated Learning

Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning

no code implementations • 22 Nov 2023 • Dengke Yan, Ming Hu, Zeke Xia, Yanxin Yang, Jun Xia, Xiaofei Xie, Mingsong Chen

However, due to data heterogeneity and stragglers, SFL suffers from the challenges of low inference accuracy and low efficiency.

Federated Learning

Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation

no code implementations • 22 Aug 2023 • Yanxin Yang, Ming Hu, Yue Cao, Jun Xia, Yihao Huang, Yang Liu, Mingsong Chen

By using these trigger images, our approach eliminates poisoned models to ensure the updated global model is benign.

Backdoor Attack Federated Learning +1