Federated learning model exchange. Proposed model transformation and control with secure model exchange with model bootstrapping. Controlling the different types of model handlingmodel-handling activities requires the model-based encrypted defense mechanism. Using this technique, we trained the model to make accurate decisions local training data with model bootstrapping. During the training process, limited participants in the local node multilevel data validations. Developing a secure model distribution for defense in the proposed multi-layered paradigm includes IoT, blockchain technology, and federated learning. When utilizing the MANIST, Fashion-MNIST the MBFL model improved the security by 16\%16% and improved the confidentiality and integrity overhead by 29\%29% and 16\%16%, respectively, compared to existing methods. Using the CH-MNIST, Novel Corona VirusCoronavirus 2019 Dataset, the MBFL model enhanced the security by 19\%19% and enhanced the confidentiality and integrity overhead by 29\%29% and 20\%20%, respectively.
Future works will examine model bootstrapping-based decentralized, federated learning towards secure control and trust management,
and the usage control scheme of streaming data. Toto answer the security and trust issues for video and audio stream data management.

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