This review highlights the efficacy of combining federated learning (FL) and transfer learning
(TL) for cancer detection via image analysis. By integrating these techniques, research has …

Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …

Federated learning (FL) has emerged as a promising paradigm that enables clients to
collaboratively train a shared global model without uploading their local data. To alleviate …

Federated learning (FL) is a distributed learning paradigm that maximizes the potential of
data-driven models for edge devices without sharing their raw data. However, devices often …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

Federated Learning (FL) has emerged as a privacy-preserving training paradigm, which
enables distributed devices to jointly learn a shared model without raw data sharing …

Personalized federated learning (PFL) is a popular distributed learning framework that
allows clients to have different models and has many applications where clients' data are in …

A heterogeneous information network (heterogeneous graph) federated learning plays a
crucial role in enabling multiparty collaboration in the Internet of Things system. However …

Federated learning (FL) is an effective mobile edge computing framework that enables
multiple participants to collaboratively train intelligent models, without requiring large …

Artificial intelligence and the Internet of Things (IoT) have brought great convenience to
people's everyday lives. With the emergence of edge computing, IoT devices such as …