Trust-Driven Pre-Aggregation Federated Learning with Agentic Orchestration and Blockchain-Based Auditing for Secure Healthcare Systems

Federated Learning (FL) reduces direct exposure of sensitive clinical data, but decentralized healthcare deployments remain vulnerable to Byzantine and data-poisoning attacks and are complicated by non-independent and identically distributed (non-IID) data across institutions. Existing defenses typically address these problems in isolation: robustness mechanisms add computational overhead without provenance guarantees, blockchain-only integrations improve auditability at the cost of scalability, and agentic-AI orchestration approaches are rarely reconciled with healthcare interoperability standards such as FHIR and HL7. This paper proposes the Tri-Layer Agentic Federated Blockchain Architecture (TAFBA), a system-design framework that integrates privacy-preserving FL, agentic trust orchestration, and permissioned blockchain auditing in a single closed-loop pipeline. The central methodological contribution is a pre-aggregation validation pipeline (local training, differential-privacy injection, agentic trust scoring, anomaly-based rejection, and trust-weighted, cluster-aware aggregation) that departs from the aggregate-then-filter paradigm used in most federated systems. TAFBA is developed through a theoretical and system-design methodology: we formalize the threat model and an explicit adversarial-fraction assumption, derive a composite, temporally smoothed trust score, and give an analytical argument, with an asymptotic complexity analysis, showing how pre-aggregation filtering and trust weighting bound the influence of adversarial nodes. We also specify an architecture-level mapping to FHIR resources and HL7 interaction points and a reproducible protocol for future empirical validation against Byzantine-robust baselines such as Krum, Multi-Krum, and FLTrust. TAFBA does not report empirical performance figures; it provides a mathematically grounded foundation for regulatory-aware, adversary-resilient decentralized learning in clinical settings, together with a defined pathway toward empirical validation.