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**Abstract:** This study introduces a novel framework, Federated Privacy-Preserving Generative Phenotype Inference (FPP-GPI), for predicting phenotypic traits across generations while preserving genetic privacy within a federated learning paradigm. Addressing the critical challenge of data sharing limitations and privacy concerns in multi-generational genetic data transmission, FPP-GPI combines federated differential privacy (FDDP) with generative adversarial networks (GANs). The system leverages the enhanced representation capabilities of GANs to model complex genotype-phenotype relationships within each individual data source and then aggregates these models in a federated setting, injecting differential privacy at each aggregation step.

**Abstract:** This research proposes a novel framework, Federated Decentralized Differential Privacy for Real-Time User Behavioral Prediction (FDDP-RBPP), for improving the accuracy of real-time user behavioral prediction in targeted advertising while robustly preserving user privacy. FDDP-RBPP leverages a federated learning approach, decentralized gradient updates, and advanced differential privacy techniques to enable model training across distributed user devices without direct data sharing. We demonstrate that FDDP-RBPP achieves comparable prediction accuracy to centralized models while significantly reducing privacy risks, enhancing user trust and compliance with privacy regulations.

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