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

Abstract Machine learning (ML) and Deep learning (DL) models are popular in many areas,
from business, medicine, industries, healthcare, transportation, smart cities, and many more …

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

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) 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 …

Federated learning (FL) enables multiple clients to jointly train high-performance deep
learning models while maintaining the training data locally. However, it is challenging to …

Federated learning (FL) is an increasingly popular approach for machine learning (ML) in
cases where the training dataset is highly distributed. Clients perform local training on their …

Federated learning is vulnerable to poisoning attacks in which malicious clients poison the
global model via sending malicious model updates to the server. Existing defenses focus on …

Federated learning (FL) is an emerging machine learning paradigm. With FL, distributed
data owners aggregate their model updates to train a shared deep neural network …