Mai Alzamel1,*, Hamza Ali Rizvi2, Najwa Altwaijry1, Isra Al-Turaiki1
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4177-4204, 2024, DOI:10.32604/cmc.2024.048370
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… More >
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