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

Mobile devices have become an essential element in our day-to-day lives. The chances of
mobile attacks are rapidly increasing with the growing use of mobile devices. Exploiting …

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 is a machine learning paradigm that emerges as a solution to the privacy-
preservation demands in artificial intelligence. As machine learning, federated learning is …

Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …

Byzantine-robust federated learning aims at mitigating Byzantine failures during the
federated training process, where malicious participants (known as Byzantine clients) may …

In the past decades, artificial intelligence (AI) has achieved unprecedented success, where
statistical models become the central entity in AI. However, the centralized training and …

Federated learning (FL) is a machine learning (ML) technique that enables collaborative
model training without sharing raw data, making it ideal for Internet of Things (IoT) …

Federated Learning (FL) offers decentralized machine learning (ML) capabilities while
potentially safeguarding data privacy. However, this architecture introduces unique security …

In recent years, deep learning methods based on a large amount of data have achieved
substantial success in numerous fields. However, with increases in regulations for protecting …