The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

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 …

As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …

Federated models are created by aggregating model updates submittedby participants. To
protect confidentiality of the training data, the aggregator by design has no visibility into how …

Federated learning (FL) is a heavily promoted approach for training ML models on sensitive
data, eg, text typed by users on their smartphones. FL is expressly designed for training on …

Federated learning enables training collaborative machine learning models at scale with
many participants whilst preserving the privacy of their datasets. Standard federated …

In Byzantine robust distributed or federated learning, a central server wants to train a
machine learning model over data distributed across multiple workers. However, a fraction …

To improve the resilience of distributed training to worst-case, or Byzantine node failures,
several recent methods have replaced gradient averaging with robust aggregation methods …

Due to the lack of CAN frame encryption and authentication, CAN bus is vulnerable to
various attacks, which can in general be divided into message injection, suspension, and …

Byzantine-robustness has been gaining a lot of attention due to the growth of the interest in
collaborative and federated learning. However, many fruitful directions, such as the usage of …