As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …

The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual
benefits, presents a unique opportunity to unlock new possibilities in AI research, and …

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

The inevitable private information in legal data necessitates legal artificial intelligence to
study privacy-preserving and decentralized learning methods. Federated learning (FL) has …

Federated learning (FL) is an emerging distributed machine learning method that empowers
in-situ model training on decentralized edge devices. However, multiple simultaneous FL …

Owing to the nature of privacy protection, feder-ated recommender systems (FedRecs) have
garnered increasing interest in the realm of on-device recommender systems. However …

Federated learning emerges as a solution to the dilemma of data silos while safeguarding
data privacy, particularly relevant in the consumer electronics sector where user data privacy …

This survey explores the transformative impact of foundation models (FMs) in artificial
intelligence, focusing on their integration with federated learning (FL) in biomedical …

Federated learning is for training a global model without collecting private local data from
clients. As they repeatedly need to upload locally-updated weights or gradients instead …

With the increasing availability of Foundation Models, federated tuning has garnered
attention in the field of federated learning, utilizing data and computation resources from …