This paper focuses on the problem of personalized federated learning (FL) with the schema
of contrastive learning (CL), which is to implement collaborative pattern classification by …

Recent advancements in deep learning methods bring computer-assistance a step closer to
fulfilling promises of safer surgical procedures. However, the generalizability of such …

Since federated learning (FL) has been introduced as a decentralized learning technique
with privacy preservation, statistical heterogeneity of distributed data stays the main obstacle …

Semi-Supervised Learning (SSL) has received extensive attention in the domain of
computer vision, leading to development of promising approaches such as FixMatch. In …

Federated learning (FL) is a popular solution for distributed machine learning (ML). While FL
has traditionally been studied for supervised ML tasks, in many applications, it is impractical …

We consider a real-world scenario in which a newly-established pilot project needs to make
inferences for newly-collected data with the help of other parties under privacy protection …

Federated semi-supervised learning (FSSL) has been proposed to address the insufficient
labeled data problem by training models with pseudo-labeling. In previous FSSL systems, a …

The massive data from mobile and embedded devices have huge potential for training
machine learning models. Decentralized machine learning (DML) can avoid the inherent …

Federated learning (FL) is a promising approach that enables distributed clients to
collaboratively train a global model while preserving their data privacy. However, FL often …

This study investigates the challenging task of training visual models with very few available
data, further complicated by the distribution being imbalanced and scattered across nodes …