Although federated learning offers a level of privacy by aggregating user data without direct
access, it remains inherently vulnerable to various attacks, including poisoning attacks …

U-shaped networks have become prevalent in various medical image tasks such as
segmentation, and restoration. However, most existing U-shaped networks rely on …

Multiparty fully homomorphic encryption (MFHE) schemes enable multiple parties to
efficiently compute functions on their sensitive data while retaining confidentiality. However …

With the wider adoption of machine learning and increasing concern about data privacy,
federated learning (FL) has received tremendous attention. FL schemes typically enable a …

Owing to its practical configuration to edge computing and privacy preservation capabilities,
federated learning (FL) has been increasingly appealing in Internet of Things (IoT) networks …

Multiparty homomorphic encryption (MHE) enables a group of parties to encrypt data in a
way that (i) enables the evaluation of functions directly over its ciphertexts and (ii) enforces a …

U-shaped networks are widely used in various medical image tasks, such as segmentation,
restoration and reconstruction, but most of them usually rely on centralized learning and thus …

Federated learning offers a framework for collaborative machine learning without
compromising data privacy, an especially critical feature when dealing with sensitive graph …

Fueled by its successful commercialization, the recommender system (RS) has gained
widespread attention. However, as the training data fed into the RS models are often highly …

The Internet of Vehicles (IoV) connects a massive amount of smart vehicles for inter/intra-
vehicle information sharing. Data privacy issues, such as privacy leakage and privacy cost …