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Federated learning represents a paradigm-shifting solution for unlocking proprietary materials databases in artificial intelligence-driven materials science. This position paper asserts that federated learning enables privacy-preserving collaborative training across decentralized sites—industry laboratories, corporate R&D facilities, and academic institutions—without ever centralizing sensitive raw data. By allowing each participant to train locally on its own proprietary datasets while sharing only model updates, federated learning overcomes the longstanding impasse created by competitive, legal, and privacy barriers that have historically prevented meaningful data sharing in the field. Industry holds vast, high-value proprietary materials databases encompassing real-world processing conditions, rare compositions, failure modes, and multi-fidelity experimental results that are simply unavailable in public repositories. Academic models, by contrast, remain constrained by biased, narrow public datasets that limit generalization and predictive power. Federated learning bridges this divide, delivering three core benefits: genuine privacy preservation that satisfies export-control regulations and intellectual-property safeguards; unprecedented data diversity that spans industrial-scale variability; and competitive pre-training of foundation models that every participant can subsequently fine-tune for proprietary tasks. Despite acknowledged challenges—heterogeneous data distributions across clients, communication overhead, and residual security risks—the technology has already demonstrated feasibility in closely related domains such as pharmaceutical chemistry and additive manufacturing. This perspective argues that the materials science community must now prioritize infrastructure investment, standardized protocols, and cross-sector consortia to realize federated learning’s potential. Only through deliberate collective action can the field harness the full power of proprietary data while preserving each organization’s competitive edge and legal compliance. The time for pilot projects and community standards is immediate.