Federated Learning (FL) enhances decentralized machine learning by safeguarding data
privacy, reducing communication costs, and improving model performance with diverse data …

Federated learning (FL) is an emerging paradigm of privacy-preserving distributed machine
learning that effectively deals with the privacy leakage problem by utilizing cryptographic …

Federated learning is a privacy-by-design framework that enables training deep neural
networks from decentralized sources of data, but it is fraught with innumerable attack …

Federated learning is used to train a shared model in a decentralized way without clients
sharing private data with each other. Federated learning systems are susceptible to …

As an emerging distributed machine learning (ML) method, federated learning (FL) can
protect data privacy through collaborative learning of artificial intelligence (AI) models across …

Federated learning is inherently vulnerable to model poisoning attacks because its
decentralized nature allows attackers to participate with compromised devices. In model …

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

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 has emerged as an advanced privacy-preserving learning technique for
mobile edge computing, where the model is trained in a decentralized manner by the clients …

Along with providing several benefits, the unprecedented growth of connected and
automated vehicles brings worries about damaging cyber attacks. Network-based intrusion …