Supervised deep learning needs a large amount of labeled data to achieve high
performance. However, in medical imaging analysis, each site may only have a limited …

In this article, we propose a new paradigm of Federated Learning (FL) for Internet of Things
(IoT) devices called Coalitional Federated Learning. The proposed paradigm aims to …

Federated Learning (FL) is an AI framework that enables collaborative and distributed
training across multiple users to learn a global model while preserving the privacy of the …

We study the personalized federated learning problem under asynchronous updates. In this
problem, each client seeks to obtain a personalized model that simultaneously outperforms …

In federated learning, particularly in cross-device scenarios, secure aggregation has
recently gained popularity as it effectively defends against inference attacks by malicious …

This thesis studies collaborative learning framework, where a group of agents cooperate to
learn a powerful model from their local data in a distributed or decentralized manner. We …

The abundance of Internet of Things (IoT) devices calls for resilient security measures
capable of detecting abnormal activities that signal possible cyber threats. Nevertheless …