Federated learning (FL) has emerged as a promising solution to address the challenges
posed by data silos and the need for global data fusion. It offers a distributed machine …

Federated Learning (FL), while a breakthrough in decentralized machine learning, contends
with significant challenges such as limited data availability and the variability of …

Federated learning (FL) has emerged as a prominent paradigm enabling collaborative
model training without transmitting local data, thereby safeguarding data privacy. However …

The Internet of Things (IoT) is revolutionizing numerous industrial applications by employing
smart devices in manufacturing and industrial processes. Industries based on IoT generate …

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) …

This paper introduces an advanced approach for fortifying Federated Learning (FL) systems
against label-flipping attacks. We propose a simplified consensus-based verification process …

In recent years, privacy concerns have become a serious issue for companies wishing to
protect economic models and comply with end-user expectations. In the same vein, some …

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

Backdoor attacks against Federated Learning (FL) are highly disguised, persistent and
mutable because the openness of participants increases the attacker's configurable space …

Federated learning has emerged as a promising paradigm in the domain of distributed
artificial intelligence (AI) systems, enabling collaborative model training across …