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

Computers, Material. & Continua 2024年第3期78卷 4177-4204页

作者： Mai al.amel hamza al. rizvi Najwa al.waijry Isra al.Turaiki Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadhKingdom of Saudi Arabia Department of Computer Science and Engineering Punjab Engineering CollegeChandigarhIndia

Scal.bility and information personal.privacy are vital.for training and deploying large-scal. deep learning *** learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web ***,relying on a main central.server for internet browser-based federated systems can prohibit scal.bility and interfere with the training process as a result of growing client ***,information relating to the training dataset can possibly be extracted from the distributed weights,potential.y reducing the privacy of the local.data used for *** this research paper,we aim to investigate the chal.enges of scal.bility and data privacy to increase the efficiency of distributed training *** a result,we propose a web-federated learning exchange(WebFLex)framework,which intends to improve the decentral.zation of the federated learning *** is additional.y developed to secure distributed and scal.ble federated learning systems that operate in web browsers across heterogeneous ***,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.*** has actual.y been measured in various setups using the MNIST *** results show WebFLex’s ability to improve the scal.bility of federated learning systems,al.owing a smooth increase in the number of participating devices without central.data *** addition,WebFLex can maintain a durable federated learning procedure even when faced with device disconnections and network ***,it improves data privacy by utilizing artificial.noise,which accomplishes an appropriate bal.nce between accuracy and privacy preservation.

关键词： Federated learning web browser privacy deep learning

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