Federated learning (FL) has nourished a promising method for data silos, which enables
multiple participants to construct a joint model collaboratively without centralizing data. The …

Federated learning (FL) has nourished a promising scheme to solve the data silo, which
enables multiple clients to construct a joint model without centralizing data. The critical …

With the emergence of data silos and popular privacy awareness, the traditional centralized
approach of training artificial intelligence (AI) models is facing strong challenges. Federated …

Federated Learning (FL) is a distributed, and decentralized machine learning protocol. By
executing FL, a set of agents can jointly train a model without sharing their datasets with …

Abstract Empirical attacks on Federated Learning (FL) systems indicate that FL is fraught
with numerous attack surfaces throughout the FL execution. These attacks can not only …

Data is readily available with the growing number of smart and IoT devices. However,
application-specific data is available in small chunks and distributed across demographics …

Federated learning (FL) serves as an enabling technology for intelligent edge computing,
where high-quality machine learning (ML) models are collaboratively trained over large …

Federated learning (FL) is an approach within the realm of machine learning (ML) that
allows the use of distributed data without compromising personal privacy. In FL, it becomes …

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

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 …