Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data-Reference-Cited by-同舟云学术

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Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data

Published:2023-10-16 Issue: Volume: Page:
ISSN:
Container-title:2023 7th Cyber Security in Networking Conference (CSNet)
language:
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Author:

Arafeh Mohamad¹,Wazzeh Mohamad¹,Ould-Slimane Hakima²,Talhi Chamseddine¹,Mourad Azzam³,Otrok Hadi⁴

Affiliation:

1. Ecole de Technologie Superieure (ETS),Department of Software and IT engineering,Montreal,Canada

2. Universite de Quebec a Trois-Rivieres (UQTR),Department of Mathematics and Computer Science,Canada

3. New York University,Division of Science,Abu Dhabi,UAE

4. Center of Cyber-Physical Systems (C2PS), Khalifa University,Department of EECS,Abu Dhabi,UAE

Funder

Department of National Defence

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10339719/10339694/10339772.pdf?arnumber=10339772

Reference16 articles.

1. Independent and Identically Distributed (IID) Data Assessment in Federated Learning

2. ModularFed: Leveraging modularity in federated learning frameworks

3. Data independent warmup scheme for non-IID federated learning

4. Improving the Communication and Computation Efficiency of Split Learning for IoT Applications

5. Communication-efficient federated learning

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication;Journal of Network and Computer Applications;2024-11

2. Securing Web Data and Privacy in AIoT Systems;Advances in Web Technologies and Engineering;2024-05-16

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