Motivated by the heterogeneous nature of devices participating in large-scale federated
learning (FL) optimization, we focus on an asynchronous server-less FL solution …

Federated Learning (FL) aims to foster collaboration among a population of clients to
improve the accuracy of machine learning without directly sharing local data. Although there …

Federated learning, a privacy-preserving collaborative machine learning paradigm, has led
to the proposal of various incentive mechanisms to encourage active participation of data …

Limited access to large-scale data is a key obstacle to building machine learning (ML)
applications in practice, partly due to a reluctance of information exchange among data …

Modern manufacturing value chains require intelligent orchestration of processes across
company borders in order to maximize profits while fostering social and environmental …

Federated learning is envisioned to be a key enabler of network functionalities based on
artificial intelligence. Multiple access mechanisms supporting the learning task must then be …

Federated learning (FL) has emerged as a promising framework for collaborative machine
learning, allowing the training of machine learning models on distributed devices without …

Gas usage estimation plays a critical role in various aspects of the power generation and
delivery business, including budgeting, resource planning, and environmental preservation …

Hierarchical federated learning (HFL) can effectively alleviate the communication bottleneck
of traditional federated learning. However, the long-term healthy development of federated …

Federated Learning (FL) is an emerging field of research that contributes to collaboratively
training machine learning models by leveraging idle computing resources and sensitive …