Federated Learning for Cross-Institutional Phytochemical Discovery: Collaborative Model Development Without Centralized Sensitive Data Sharing

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Cross-institutional phytochemical discovery increasingly depends on distributed chemical, biological, omics, safety, provenance, and proprietary datasets that cannot always be transferred to a central repository. Although federated learning may enable participating institutions to develop shared computational models while retaining source data locally, data locality alone does not resolve privacy leakage, cybersecurity, data heterogeneity, scientific validity, governance, or institutional-trust concerns. This article proposes a conceptual federated learning architecture for collaborative phytochemical discovery across natural product laboratories, academic institutions, pharmaceutical research groups, bioinformatics centers, and other authorized partners. The architecture links consortium formation and data-governance agreements with local data stewardship, metadata harmonization, feature and label alignment, local model training, governed model-update exchange, global aggregation, privacy and security safeguards, cross-site validation, bias assessment, expert review, model documentation, lifecycle governance, and structured feedback. It accommodates phytochemical records, chemical representations, bioactivity and assay data, target and pathway annotations, omics evidence where supported, ADMET and toxicity information, proprietary compound libraries, and carefully governed traditional knowledge, biodiversity, clinical, or pharmacovigilance signals where appropriate. The principal contribution is an architecture that treats federated learning as one component of a broader scientific and governance system rather than as an automatic privacy or discovery solution. The proposed framework supports research collaboration and hypothesis prioritization but does not establish privacy certification, security certification, therapeutic validity, clinical utility, regulatory readiness, or operational deployment.