Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to
overcome challenges of data silos and data sensibility. Exactly what research is carrying the …

Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with
different features about the same set of users jointly train machine learning models without …

Transformers have made remarkable progress towards modeling long-range dependencies
within the medical image analysis domain. However, current transformer-based models …

The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT
equipments generating massive amounts of user data every moment. According to the …

Federated learning (FL), which provides a collaborative training scheme for distributed data
sources with privacy concerns, has become a burgeoning and attractive research area. Most …

Federated learning enables clients to collaboratively learn a shared global model without
sharing their local training data with a cloud server. However, malicious clients can corrupt …

Recent studies on contrastive learning have achieved remarkable performance solely by
leveraging few labels in medical image segmentation. Existing methods mainly focus on …

With the increasing amount of multimedia data on modern mobile systems and IoT
infrastructures, harnessing these rich multimodal data without breaching user privacy …

Contrastive learning (CL) aims to learn useful representation without relying on expert
annotations in the context of medical image segmentation. Existing approaches mainly …