Federated learning (FL) and split learning (SL) are two emerging collaborative learning
methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT) …

Recent developments in deep learning have contributed to numerous success stories in
healthcare. The performance of a deep learning model generally improves with the size of …

The increasingly deeper neural networks hinder the democratization of privacy-enhancing
distributed learning, such as federated learning (FL), to resource-constrained devices. To …

Vertical federated learning (VFL) is a promising category of federated learning for the
scenario where data is vertically partitioned and distributed among parties. VFL enriches 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 …

Projection maintenance is one of the core data structure tasks. Efficient data structures for
projection maintenance have led to recent breakthroughs in many convex programming …

Split learning, a distributed learning framework, has garnered significant attention from
academic and industrial communities. In contrast to federated learning, split learning offers a …

In recent years, privacy and security concerns in machine learning have promoted trusted
federated learning to the forefront of research. Differential privacy has emerged as the de …

Vertical federated learning (VFL) is being used more and more widely in industry. One of its
most common application scenarios is a two-party setting: a participant (ie, the host), who …

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm
where different parties collaboratively learn models using partitioned features of shared …