With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an important component of our daily life, providing personalized suggestions …

Federated learning (FL) is a promising framework for distributed machine learning that trains
models without sharing local data while protecting privacy. FL exploits the concept of …

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

Recommender systems have been widely used in different application domains including
energy-preservation, e-commerce, healthcare, social media, etc. Such applications require …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

Federated recommender system (FRS), which enables many local devices to train a shared
model jointly without transmitting local raw data, has become a prevalent recommendation …

The marriage of federated learning and recommender system (FedRec) has been widely
used to address the growing data privacy concerns in personalized recommendation …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

Collaborative filtering (CF) based recommender systems are typically trained based on
personal interaction data (eg, clicks and purchases) that could be naturally represented as …

Pre-trained vision-language models like CLIP show great potential in learning
representations that capture latent characteristics of users. A recently proposed method …