Secure Approach for Cloud: A Comprehensive Survey of Security Measures
Keywords:
Cloud, Federated Learning (FL), Service Level Agreement (SLA), Quality Of Service (QoS)Abstract
Clients using federated learning can learn a global model simultaneously without submitting their own training data to a central server in the cloud. Malicious clients, however, can potentially damage the global model, leading to inaccurate predictions of legal test labels. Federated Learning (FL) protects user privacy by transmitting information from a centralized server to individual endpoints, allowing AI to be applied to areas with sensitive information and heterogeneity. In addition, iterative local model training methods on end-devices allow computing resources to be shared among the involved parties as opposed to depending on a centralized server. One of the extremely fast fields of machine learning, FL is promising to keep up with new regulations protecting user data according to its decentralized data model. In addition to protecting users' confidentiality, FL provides the benefits of ML to specialized areas where there is not enough data for a stand-alone ML model to be developed. As a result of FL's reputation for protecting user privacy, the technology is increasingly being used by industries that process sensitive information. However, Hackers and interested attackers can utilize the federated environment in novel ways because of the increased number of training variants, communications, and the exchange of model parameters. This can be used to influence the ML model's output or gain access to private user data. This research survey explores different technologies emerged for secure cloud environment with FL and ML terminology
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