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...

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Vydané v:	IEEE internet of things journal Ročník 11; číslo 8; s. 13180 - 13198
Hlavní autori:	Hidayat, Muhammad Ayat, Nakamura, Yugo, Arakawa, Yutaka
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 15.04.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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