The Internet of Things (IoT) has created a ubiquitously connected world powered by a
multitude of wired and wireless sensors generating a variety of heterogeneous data over …

The cloud-edge-terminal collaborative network (CETCN) is considered as a novel paradigm
for emerging applications owing to its huge potential in providing low-latency and ultra …

This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL
is a recent distributed training scheme where multiple clients send intermediate activations …

As mobile devices are becoming ubiquitous, regularly interacting with a variety of user
interfaces (UIs) is a common aspect of daily life for many people. To improve the …

Split learning is privacy-preserving distributed learning that has gained momentum recently.
It also faces new security challenges. FSHA [37] is a serious threat to split learning. In FSHA …

Collaborative learning has gained great popularity due to its benefit of data privacy
protection: participants can jointly train a Deep Learning model without sharing their training …

Distributed learning is proposed as a promising technique to reduce heavy data
transmissions in centralized machine learning. By allowing the participants training the …

Bit-flip attacks (BFAs) have attracted substantial attention recently, in which an adversary
could tamper with a small number of model parameter bits to break the integrity of DNNs. To …

With the prevalence of the Internet of Things (IoT) systems, smart cities comprise complex
networks, including sensors, actuators, appliances, and cyber services. The complexity and …

Federated learning (FL) provides convenience for cross-domain machine learning
applications and has been widely studied. However, the original FL is still vulnerable to …