Open Access

ARTICLE

WebFLex: A Framework for Web Browsers-Based Peer-to-Peer Federated Learning Systems Using WebRTC

Mai Alzamel^1,*, Hamza Ali Rizvi², Najwa Altwaijry¹, Isra Al-Turaiki¹

1 Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia
2 Department of Computer Science and Engineering, Punjab Engineering College, Chandigarh, India

* Corresponding Author: Mai Alzamel. Email:

Computers, Materials & Continua 2024, 78(3), 4177-4204. https://doi.org/10.32604/cmc.2024.048370

Received 06 December 2023; Accepted 05 February 2024; Issue published 26 March 2024

Abstract

Scalability and information personal privacy are vital for training and deploying large-scale deep learning models. Federated learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web browsers. Nevertheless, relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client numbers. Additionally, information relating to the training dataset can possibly be extracted from the distributed weights, potentially reducing the privacy of the local data used for training. In this research paper, we aim to investigate the challenges of scalability and data privacy to increase the efficiency of distributed training models. As a result, we propose a web-federated learning exchange (WebFLex) framework, which intends to improve the decentralization of the federated learning process. WebFLex is additionally developed to secure distributed and scalable federated learning systems that operate in web browsers across heterogeneous devices. Furthermore, WebFLex utilizes peer-to-peer interactions and secure weight exchanges utilizing browser-to-browser web real-time communication (WebRTC), efficiently preventing the need for a main central server. WebFLex has actually been measured in various setups using the MNIST dataset. Experimental results show WebFLex’s ability to improve the scalability of federated learning systems, allowing a smooth increase in the number of participating devices without central data aggregation. In addition, WebFLex can maintain a durable federated learning procedure even when faced with device disconnections and network variability. Additionally, it improves data privacy by utilizing artificial noise, which accomplishes an appropriate balance between accuracy and privacy preservation.

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Keywords

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