The problem of Byzantine resilience in distributed machine learning, aka Byzantine machine
learning, consists of designing distributed algorithms that can train an accurate model …

Byzantine-robust federated learning aims to enable a service provider to learn an accurate
global model when a bounded number of clients are malicious. The key idea of existing …

Due to the greatly improved capabilities of devices, massive data, and increasing concern
about data privacy, Federated Learning (FL) has been increasingly considered for …

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 …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

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

Existing model poisoning attacks to federated learning assume that an attacker has access
to a large fraction of compromised genuine clients. However, such assumption is not realistic …

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 …

Byzantine resilience emerged as a prominent topic within the distributed machine learning
community. Essentially, the goal is to enhance distributed optimization algorithms, such as …

Federated learning enables clients to collaboratively learn a shared global model without
sharing their local training data with a cloud server. However, malicious clients can corrupt …