Federated Recommender Systems for Privacy-Preserving Personalized Nutritional Peptide Intervention

Abstract

Personalized nutritional peptide interventions promise to align dietary bioactive compounds with individual metabolic, immunological, and aging-related health trajectories. Yet the data required for such personalization are intimate, longitudinal, and clinically sensitive, ranging from dietary logs and wearable signals to biomarker panels and gastrointestinal symptoms. Centralized recommender architectures create privacy, regulatory, and trust liabilities that are difficult to reconcile with precision nutrition. This paper examines federated recommender systems as a socio-technical infrastructure for privacy-preserving personalized nutritional peptide intervention. It argues that federated learning alone is insufficient; practical systems require secure aggregation, differential privacy, robust personalization, human oversight, and governance across clinical, commercial, and consumer domains. The analysis develops a system-level architecture that distributes model training across households, clinics, wearable ecosystems, and peptide knowledge bases while keeping raw data local. It then evaluates structural trade-offs among privacy, accuracy, latency, fairness, explainability, and sustainability. Particular attention is given to non-identical data distributions, sparse peptide-outcome evidence, adversarial manipulation, equity gaps, and the policy implications of treating nutrition as a quasi-clinical intervention. The paper concludes that federated recommender systems can support credible peptide personalization only when technical privacy mechanisms are coupled with participatory governance, auditable evidence pipelines, edge-aware deployment, and fairness-aware evaluation. Such systems should be designed as adaptive public-interest infrastructures rather than opaque commercial engines, enabling responsible innovation in nutritional peptide interventions while preserving individual and community trust.