Privacy-Preserving Federated Learning Framework for Distributed Chemical Process

arXiv:2603.25334v1 Announce Type: new Abstract: Distributed intelligence in industrial networks increasingly integrates sensing, communication, and computation across

arXiv:2608.11281v1 Announce Type: cross Abstract: Tool wear prediction is an important task in CNC machining, where accurate monitoring of tool condition supports product quality

arXiv:2607.06612v1 Announce Type: cross Abstract: Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality,

arXiv:2504.15995v2 Announce Type: replace-cross Abstract: Vertical Federated Learning (VFL) enables organizations with disjoint feature spaces but shared user bases to

arXiv:2607.01366v1 Announce Type: new Abstract: Federated learning (FL) research often depends on many small but consequential algorithmic choices: optimizer variants, server

arXiv:2605.00011v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative intelligence across decentralized data source devices in a privacy-preserving way.

arXiv:2608.11532v1 Announce Type: cross Abstract: In recent research on the Digital Twin-based Vehicular Ad hoc Network(DT-VANET), Federated Learning (FL) has shown its ability to

arXiv:2603.22366v1 Announce Type: cross Abstract: We propose a Quantum Federated Autoencoder for Anomaly Detection, a framework that leverages quantum federated learning for