This paper argues that distributed learning—including federated and decentralized approaches—should become the primary paradigm for large language model (LLM) post‑training rather than a mere privacy add‑on. Existing methods such as RLHF and DPO implicitly assume that preference data must be centralized, an assumption that lacks scientific justification, is practically unsustainable, and raises ethical concerns. As LLMs move into healthcare, law, finance, and multilingual communities, preference data originates from populations that cannot or will not centralize it. We therefore build a three‑pillar case—privacy and data sovereignty: distributed training keeps sensitive information locally and satisfies regulatory demands; pluralism and representational equity: diverse cultural, linguistic, and legal preferences remain local yet jointly shape the model; robustness and scalability: distributed architectures naturally handle massive heterogeneous data and avoid single‑point failures. We formalize the unique technical challenges at this intersection, including concept drift, preference heterogeneity, and the differential‑privacy versus utility trade‑off for contrastive losses. We then propose algorithmic directions such as secure multi‑party computation for gradient aggregation, noise‑injected contrastive learning, and adaptive weight‑balancing mechanisms, and sketch a research agenda. Our analysis shows that distributed post‑training is not only necessary but also tractable; ignoring it risks legal non‑compliance, narrow representation, and brittleness at scale. Review