ABSTRACT
Data security has emerged as the main type of protection in the big data era, as practically all information is sent across networks. There are still issues with privacy and the leakage of sensitive information, despite the significant advancements in network information security protection mechanisms. A combined model is stored on a cloud server, while the training data are scattered across several devices. Customers may benefit from the collaborative training technique with federated learning, a groundbreaking machine learning design that safeguards the confidentiality of their personal data. A potential method that supports distributed collaborative learning without revealing the original training data is federated learning. The information is then utilised to develop insightful inference models or to provide insights. Federated learning, a ground-breaking technique, addresses the problem of safeguarding personal information via the security measures mechanism by enabling consumers to collectively develop a global model while maintaining local control over the data.
