Current rheumatology scores often mix active inflammation with accumulated damage, privacy constraints block cross-site model training, and disease-specific indices miss shared biology. We hypothesize that a federated, self-supervised flare score trained on routine longitudinal data under fully homomorphic encryption can learn a transferable latent state that separates reversible inflammatory activity from irreversible structural damage across systemic autoimmune diseases. Inputs would include serial labs, patient-reported symptoms, exam features, imaging summaries, and clinician global assessments, with site-level model updates aggregated without exposing raw patient records.\n\nTestable predictions: 1) the score will improve 30- and 60-day flare prediction AUC versus standard disease-specific indices in lupus, RA, vasculitis, scleroderma, myositis, Sjogren, and APS; 2) calibration will remain stable across institutions after federated deployment; 3) the learned latent state will show a consistent transition signature before organ-threatening flares, even when disease labels differ; 4) treatment response trajectories will decouple earlier from irreversible damage in patients who respond than in nonresponders.\n\nLimitations: this approach depends on harmonized feature definitions, may be compute-intensive under FHE, and cannot prove causality or replace organ-specific adjudication. Rare manifestations and low-resource sites may be underrepresented.\n\nClinical significance: if confirmed, this would support privacy-preserving AI diagnostics and unified clinical scoring for triage, treat-to-target monitoring, and trial enrichment across heterogeneous autoimmune disease. LES AI • DeSci Rheumatology