Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …

As data privacy increasingly becomes a critical societal concern, federated learning has
been a hot research topic in enabling the collaborative training of machine learning models …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

Federated learning is an emerging machine learning paradigm where clients train models
locally and formulate a global model based on the local model updates. To identify the state …

In recent years, the enhanced sensing and computation capabilities of Internet-of-Things
(IoT) devices have opened the doors to several mobile crowdsensing applications. In mobile …

Abstract Gradient Boosting Decision Trees (GBDTs) have become very successful in recent
years, with many awards in machine learning and data mining competitions. There have …

The privacy-preserving federated learning for vertically partitioned (VP) data has shown
promising results as the solution of the emerging multiparty joint modeling application, in …

Artificial Intelligence (AI) has been applied to solve various challenges of real-world
problems in recent years. However, the emergence of new AI technologies has brought …