@inproceedings{e340018de7ca4c62a563d70d3a7100b3,
title = "Efficient Privacy-Preserving ML for IoT: Cluster-Based Split Federated Learning Scheme for Non-IID Data",
abstract = "This paper proposes a scheme addressing the challenges of integrating privacy-preserving distributed machine learning in the Internet of Things (IoT) context while improving the efficiency of the learning process and accelerating the convergence of the model. Specifically, it addresses the presence of non-independent and identically distributed (Non-IID) data and device heterogeneity, which occurs during parallel training and are common characteristics of IoT environments. In this context, SplitFed is a privacy-preserving technique that combines Federated Learning (FL) and Split Learning (SL) to enable fast and privacy-preserving model training on IoT devices. Through parallelism, FL allows fast and parallel model training. Furthermore, SL enables weak devices to train models through model splitting. However, SplitFed suffers from the increasing convergence time due to the FL parallelism introducing Non-IID and stragglers issues. In this paper, we aim to address the aforementioned problems by proposing a clustering parallelization scheme. As such, similar clients are combined based on their weight divergence and considered a single FL unit addressing the Non-IID problem. Moreover, to improve time efficiency and overcome the stragglers' presence, we offer a second clustering stage that separates clients by efficiency and uses FedAsync for model aggregation.",
keywords = "Clustering, Federated Learning, Machine Learning, Non-IID, Participants Selection, Privacy, Split Learning",
author = "Mohamad Arafeh and Mohamad Wazzeh and Hakima Ould-Slimane and Chamseddine Talhi and Azzam Mourad and Hadi Otrok",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 7th Cyber Security in Networking Conference, CSNet 2023 ; Conference date: 16-10-2023 Through 18-10-2023",
year = "2023",
doi = "10.1109/CSNet59123.2023.10339772",
language = "British English",
series = "2023 7th Cyber Security in Networking Conference, CSNet 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "143--147",
booktitle = "2023 7th Cyber Security in Networking Conference, CSNet 2023",
address = "United States",
}