Federated Learning Privacy Protection and Resource Collaborative Optimization Mechanism for Industrial Internet of Things
Abstract
In the industrial Internet of Things environment, federated learning faces dual challenges of privacy leakage risk and resource constraints. Existing methods find it difficult to achieve an effective balance between heterogeneous data, limited communication and computing resources, and differentiated privacy requirements. This article systematically analyzes the privacy vulnerabilities of federated learning in the industrial Internet of Things, revealing the amplification effect of data heterogeneity on inference attacks and the limitations of existing protection methods. Furthermore, design privacy protection mechanisms for industrial scenarios, including hierarchical heterogeneous differential privacy noise adaptation, trusted execution environment security aggregation, dynamic allocation of privacy budgets, and adversarial robustness enhancement strategies. On this basis, a communication computing storage resource coupling constraint model is constructed to quantify the trade-off between privacy protection strength and training energy consumption, and a multi-objective optimization scheduling strategy and adaptive joint regulation protocol are proposed. The proposed mechanism provides a systematic theoretical framework for privacy, security, and resource collaboration in federated learning in the industrial Internet of Things.
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