Communication-Efficient Privacy-Preserving Federated Learning via Knowledge Distillation for Human Activity Recognition Systems-Reference-Cited by-同舟云学术

Communication-Efficient Privacy-Preserving Federated Learning via Knowledge Distillation for Human Activity Recognition Systems

Published:2023-05-28 Issue: Volume: Page:
ISSN:
Container-title:ICC 2023 - IEEE International Conference on Communications
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Author:

Gad Gad¹,Fadlullah Zubair Md²,Rabie Khaled³,Fouda Mostafa M.⁴

Affiliation:

1. Lakehead University,Department of Computer Science,Thunder Bay,Ontario,Canada

2. Western University,Department of Computer Science,London,ON,Canada

3. Manchester Metropolitan University,Department of Engineering,Manchester,UK

4. Idaho State University,Department of Electrical and Computer Engineering,Pocatello,ID,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10278505/10278554/10278987.pdf?arnumber=10278987

Reference22 articles.

1. A review of applications in federated learning

2. Robust Enhancement of Intrusion Detection Systems Using Deep Reinforcement Learning and Stochastic Game

3. Sensor-Based Abnormal Human-Activity Detection

4. Communication-efficient learning of deep networks from decentralized data;mcmahan;Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS),0

5. FedMD: Heterogenous federated learning via model distillation;li;ArXiv Preprint,2019

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