Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Additive manufacturing (AM) provides a data-rich environment for collecting a variety of
process data. These crucial data can be used to develop effective machine learning (ML) …

Between the changing Industry 4.0 landscape and the rise of Industry 5.0, where human
intelligence and intelligent machines work together, vast amounts of privacy-sensitive data …

Federated Learning (FL) has gained significant attention because of its potential for privacy-
preserving distributed learning. However, statistical heterogeneity and label scarcity remain …

Federated learning is a privacy-preserving distributed framework that facilitates information
fusion and sharing among different clients, enabling the training of a global model without …

Federated learning is a distributed paradigm that allows multiple parties to collaboratively
train deep learning models without direct exchange of raw data. Nevertheless, the inherent …

Federated learning is a collaborative machine learning framework where multiple clients
jointly train a global model. To mitigate communication overhead, it is common to select a …

Federated Learning (FL), despite its success as a privacy-preserving distributed machine
learning framework, faces significant bottlenecks, including high communication costs …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

Fintech is an industry that uses technology to enhance and automate financial services.
Fintech firms use software, mobile apps, and digital technologies to provide financial …