FedTrustAds: A Federated and Explainable Advertising Attribution Framework with Backdoor-Resilient Learning for Privacy-Preserving Social Commerce Ecosystems
Keywords:
federated learning, advertising attribution, explainable AI, backdoor resilience, privacy-preserving machine learning, social commerce, content governanceAbstract
The proliferation of social commerce ecosystems has created an urgent need for advertising attribution systems that respect user privacy while maintaining accuracy, transparency, and robustness against adversarial threats. Existing centralized attribution models collect vast amounts of user interaction data, raising significant privacy concerns and creating single points of failure for malicious attacks. Federated learning offers a promising paradigm for distributed model training without raw data sharing, yet its application to advertising attribution introduces unique challenges: heterogeneous user behavior patterns, non-IID data distributions, and susceptibility to backdoor attacks that can covertly manipulate attribution outcomes. This paper presents FedTrustAds, a federated and explainable advertising attribution framework designed for privacy-preserving social commerce environments. FedTrustAds integrates three core innovations: a hierarchical federated aggregation mechanism that adapts to local data heterogeneity, a post-hoc explainability module that generates per-prediction attribution narratives using integrated gradients and counterfactual reasoning, and a backdoor-resilient learning protocol that combines anomaly detection on client updates with robust aggregation techniques to neutralize poisoning attempts. The framework is positioned within a broader socio-technical infrastructure that includes compliance-by-design content governance, standardized recommendation APIs, and trusted AI commercialization pathways for small and medium businesses. Through a detailed architectural discussion and comparative analysis with existing alternatives, we demonstrate that FedTrustAds achieves strong privacy guarantees, high attribution accuracy, and resilience against state-of-the-art backdoor attacks while preserving interpretability essential for regulatory compliance and advertiser trust. The paper concludes with policy implications and deployment strategies for multi-tenant platforms.
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