Federated learning emerges as a solution to the dilemma of data silos while safeguarding
data privacy, particularly relevant in the consumer electronics sector where user data privacy …

With the increasing attention on Machine Learning applications, more and more companies
are involved in implementing AI components into their software products in order to improve …

Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …

In recent years, federated learning (FL) plays an important role in data privacy-sensitive
scenarios to perform learning works collectively without data exchange. However, due to the …

Federated learning has received attention for its efficiency and privacy benefits, in settings
where data is distributed among devices. Although federated learning shows significant …

Federated Learning (FL) allows training machine learning models in privacy-constrained
scenarios by enabling the cooperation of edge devices without requiring local data sharing …

The widespread adoption of Federated Learning (FL), a privacy-preserving distributed
learning methodology, has been impeded by the challenge of high communication …

The widespread deployment of machine learning applications in ubiquitous environments
has sparked interests in exploiting the vast amount of data stored on mobile devices. To …

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

With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …