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 …
H Zhang, K Zeng, S Lin - IEEE Transactions on Information …, 2023 - ieeexplore.ieee.org
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 …