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 …

Federated learning (FL) is a distributed machine learning approach that can achieve the
purpose of collaborative learning from a large amount of data that belong to different parties …

This article proposes an efficient federated distillation learning system (EFDLS) for multitask
time series classification (TSC). EFDLS consists of a central server and multiple mobile …

Data privacy and silos are nontrivial and greatly challenging in many real-world
applications. Federated learning is a decentralized approach to training models across …

The proliferation of advanced embedded and communication technologies has facilitated
the possibility of modern Intelligent Transportation System (ITS). The hierarchical nature of …

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 …

Abstract Machine learning (ML) and Deep learning (DL) models are popular in many areas,
from business, medicine, industries, healthcare, transportation, smart cities, and many more …

With the widespread use of Internet of things (IoT), mobile phones, connected devices and
artificial intelligence (AI), recommender systems (RSs) have become a booming technology …

Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine
learning paradigm, is a promising approach to enable edge intelligence in the emerging …

Abstract Recommender Systems (RSs) have assumed a crucial role in several digital
companies by directly affecting their key performance indicators. Nowadays, in this era of big …