Federated Learning Incentivize with Privacy-Preserving for IoT in Edge Computing in the Context of B5G

Abstract Federated learning and edge computing have resulted in the broad adoption of internet of Things (IoT) due to their fast reaction times and low connection costs. In general, edge computing requires users to send raw data to a central server for further processing. However, this data frequently contains sensitive information that individuals may not want to disclose. As a result, transferring user data with sensitive information increases the risk of data leakage when various unauthorized devices access it. Integrating federated learning with edge computing improves privacy by creating a consistent deep learning model across devices, eliminating the need for real data transferring but the complexity and heterogeneity of the IoT environment present challenges like privacy, low communication, incentives, and seamless data aggregation. In this work, we concentrate on improving privacy with communication stability and designing incentive mechanisms to motivate more clients to participate in the model training process to enhance the performance and accuracy of data aggregation in a highly trusted environment. As a result, the novelty point is the development of a framework that combined a blockchain with federated edge computing in the context of beyond 5G to address the aforementioned challenges and provide excellent communicative and trusted environment. The study's rigorous evaluation showed that the integration of blockchain and B5G technology significantly improved the overall process of federated edge computing including increased accuracy, prevented loss, and motivated more clients to participate in the training process.