Privacy-Preserving Predictive Computing for Generative AI: A Federated and Privacy-Aware Framework for Forecasting Future Computational Demand Without Exposing User Information

The increasing deployment of generative artificial intelligence (AI) systems has created substantial opportunities for predictive computational optimization. By analyzing historical request patterns, emerging information trends, and user interaction signals, generative AI infrastructures may anticipate future computational demand and prepare appropriate resources before actual requests arrive. However, the predictive analysis of user behavior introduces an important privacy challenge. Historical queries, interaction frequencies, contextual preferences, temporal usage patterns, and inferred information interests may contain sensitive personal information. Centralizing these signals to improve demand prediction can expose users to profiling, membership inference, behavioral reconstruction, and unauthorized information disclosure. Consequently, predictive computing must address not only whether future computational demand can be accurately anticipated, but also whether such anticipation can be achieved without unnecessarily exposing the private information underlying user behavior. This paper introduces Privacy-Preserving Predictive Computing for Generative AI (PPPC-GAI), a conceptual framework that integrates privacy-preserving demand forecasting with predictive inference preparation and computational resource orchestration. The framework investigates how generative AI systems can estimate future computational requirements using distributed, aggregated, anonymized, and privacy-protected information signals while minimizing the exposure of individual user interactions. Its architecture integrates seven interconnected functions: (1) privacy-aware information signal acquisition, (2) local and federated demand forecasting, (3) privacy-preserving future workload estimation, (4) differential privacy and secure aggregation, (5) privacy-constrained predictive computational preparation, (6) adaptive privacy-budget and resource management, and (7) privacy-aware computational evaluation. Unlike conventional predictive computing architectures that may depend on centralized historical request information, PPPC-GAI investigates whether future demand can be estimated through collaborative forecasting without requiring raw user queries to be transferred to a central prediction service. The framework considers federated learning, differential privacy, secure aggregation, local statistical processing, privacy-preserving feature extraction, and aggregate demand modeling as complementary mechanisms. It further distinguishes between user-level privacy, request-level privacy, and aggregate workload information, recognizing that the protection of individual records does not automatically prevent the disclosure of sensitive population-level patterns. A unified optimization formulation is developed to jointly characterize prediction accuracy, computational preparation benefits, privacy loss, communication overhead, resource expenditure, and service-level requirements. The framework introduces privacy-adjusted predictive computational utility to determine whether a predicted workload should be prepared when both expected computational savings and potential privacy costs are considered. It also establishes a privacy-aware experimental methodology involving forecasting accuracy, differential privacy guarantees, membership inference resistance, information leakage, computational overhead, and realized inference savings. The proposed framework does not assume that federated learning, anonymization, or differential privacy independently guarantees complete privacy protection. Rather, it investigates how multiple privacy-enhancing mechanisms can be coordinated within a predictive computing architecture, with explicit consideration of adversarial threats, privacy-budget composition, model-update leakage, aggregate disclosure, and the trade-off between forecasting accuracy and privacy protection. Its practical effectiveness remains subject to empirical validation.