Decentralized Learning | Federated Learning

The content focuses on a decentralized machine learning technique that enables collaborative model training while preserving data privacy across distributed devices. Major themes include operational mechanisms, applications in sectors like healthcare and Internet of Things, and the integration of privacy measures such as differential privacy. Additionally, it addresses challenges like communication efficiency, data sovereignty, and emerging threats in cybersecurity, highlighting the potential of this approach to enhance data collaboration and security for diverse applications.

Introduction to Federated Unlearning.pptx

byHigher Education Department KP

Federated Learning – Use Case Simulation

byRavikumarR77

A Temporal Federated Meta-Learning Framework for Low-Resource Predictive Maintenance in Distributed Industrial Systems - Paper.pdf

byC Sai Kiran

Energy-Aware Federated Learning Framewor

bymnasar3

Exploring Federated Learning: Principles, Applications, and Challenges

byJamsher bhanbhro

Data & AIdvanced Machine Learning Techniques for 2025.pdf

byKodekX

EdgeShield: a robust and agile cybersecurity architecture for the internet of medical things

byTELKOMNIKA JOURNAL

Contract-based federated learning framework for intrusion detection system in internet of things networks

byIAESIJAI

Introduction to Federated Learning .pptx

bySheharBano86

Hybrid intrusion detection model for hierarchical wireless sensor network using federated learning

byIAESIJAI

Federated inception-multi-head attention models for cyber attacks detection

byIAESIJAI

Avatar for apidays apidays PRO June 07, 2025 0 5 apidays Helsinki & North 2025 - AI in Privacy tech and Consent Management, Unnikrishnan Sreedhara Kurup (Gravito)

byapidays