The prosperity of machine learning has been accompanied by increasing attacks on the
training process. Among them, poisoning attacks have become an emerging threat during …

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 …

Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

Federated learning (FL) is vulnerable to model poisoning attacks, in which malicious clients
corrupt the global model via sending manipulated model updates to the server. Existing …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

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 …

Federated learning (FL), as a distributed machine learning setting, has received
considerable attention in recent years. To alleviate privacy concerns, FL essentially …

Federated learning (FL) enables many data owners (eg, mobile devices) to train a joint ML
model (eg, a next-word prediction classifier) without the need of sharing their private training …

Abstract Empirical attacks on Federated Learning (FL) systems indicate that FL is fraught
with numerous attack surfaces throughout the FL execution. These attacks can not only …

Due to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in
the form of backdoors during training. The goal of a backdoor is to corrupt the performance …