Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT)
have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI …

Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Decentralized paradigm in the field of cybersecurity and machine learning (ML) for the
emerging Internet of Things (IoT) has gained a lot of attention from the government …

The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global
server iteratively averages the model parameters of local users without accessing their data …

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of
intelligent services and applications empowered by artificial intelligence (AI). Traditionally …

Deep learning models are often trained on distributed, webscale datasets crawled from the
internet. In this paper, we introduce two new dataset poisoning attacks that intentionally …

Fairness and robustness are two important concerns for federated learning systems. In this
work, we identify that robustness to data and model poisoning attacks and fairness …

Federated learning and analytics are a distributed approach for collaboratively learning
models (or statistics) from decentralized data, motivated by and designed for privacy …

While recent works have indicated that federated learning (FL) may be vulnerable to
poisoning attacks by compromised clients, their real impact on production FL systems is not …