Privacy-Preserving Federated Learning With Resource-Adaptive Compression for Edge Devices

Federated learning (FL) has gained widespread attention as a distributed machine learning (ML) technique that offers data protection when training on local devices. Unlike conventional centralized training in traditional ML, FL incorporates privacy and security measures as it does not share raw data...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE internet of things journal Vol. 11; no. 8; pp. 13180 - 13198
Main Authors:	Hidayat, Muhammad Ayat, Nakamura, Yugo, Arakawa, Yutaka
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 15.04.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptation models Blockchain Compression ratio Compressive sensing Computational modeling Cryptography Devices Differential privacy Federated learning federated learning (FL) Human activity recognition Machine learning Privacy privacy-preserving resource-adaptive Security Servers State of the art weight-pruning
ISSN:	2327-4662, 2327-4662
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!