Federated learning (FL) enables collaborative training of a shared model on edge devices
while maintaining data privacy. FL is effective when dealing with independent and …

The proliferation of Internet of Things (IoT) devices, generating massive amounts of
heterogeneous distributed data, has pushed toward edge cloud computing as a promising …

The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering
significant advantages in agility, responsiveness, and potential environmental benefits. The …

Conventional machine learning approaches aggregate all training data in a central server,
which causes massive communication overhead of data transmission and is also vulnerable …

With the proliferation of computationally powerful edge devices, edge computing has been
widely adopted for wide-ranging computational tasks. Among these, edge artificial …

With the wide range of Internet of Things (IoT) applications, federated learning (FL) is
commonly adopted to protect the privacy of IoT data. FL enables privacy-preserving model …

Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet
speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the …

Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Internet of Things (IoT) have widely penetrated in different aspects of modern life and many
intelligent IoT services and applications are emerging. Recently, federated learning is …

With the emergence of intelligent services and applications powered by artificial intelligence
(AI), the Internet of Things (IoT) affects many aspects of our daily lives. Traditional …