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 …

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 …

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 …

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

联邦学习作为新兴的分布式机器学习解决了数据孤岛问题. 然而, 由于大规模,
分布式特性以及本地客户端的强自主性, 使得联邦学习极易遭受拜占庭攻击且攻击不易发现 …