PrivGuard: A Privacy-Preserving and Robust Defense Framework for Multi-Scenario Learning Systems

Abstract

The rapid proliferation of multi-scenario learning systems has fundamentally transformed how collaborative machine learning models are trained across disparate domains. However, integrating heterogeneous data sources from multiple scenarios introduces profound challenges regarding data privacy and model robustness. Traditional collaborative frameworks often struggle to simultaneously mitigate sophisticated adversarial attacks and prevent inadvertent data leakage without severely degrading model utility. In response to these critical vulnerabilities, this paper introduces PrivGuard, a novel privacy-preserving and robust defense framework tailored specifically for multi-scenario learning environments. PrivGuard integrates an adaptive noise calibration mechanism with an advanced gradient anomaly detection module to secure the parameter aggregation phase. By decoupling the privacy and robustness objectives, the framework ensures that defense mechanisms against malicious actors do not inadvertently compromise the confidentiality of participating clients. Comprehensive theoretical analysis and extensive empirical evaluations demonstrate that PrivGuard consistently outperforms state-of-the-art baselines across diverse learning scenarios. The framework achieves substantial reductions in privacy leakage while maintaining high predictive accuracy under severe adversarial conditions. The results indicate that PrivGuard provides a scalable, resilient, and highly secure infrastructure for deploying collaborative machine learning models in sensitive real-world applications.