Federated Tool-Augmented LLM

A cryptographic middleware deployed at each hospital that mediates all requests between the central LLM and local tools. The gateway validates the LLM's identity, enforces role-based access control (RBAC) on tool execution, and ensures that only the computed result—never the raw patient data—leaves the institution. It typically operates within a hardened enclave or trusted execution environment (TEE) to prevent tampering by local system administrators.

An extension of standard LLM function-calling that routes tool execution requests to the correct hospital node based on a patient-context binding. When the LLM generates a function call—such as check_drug_interactions(patient_id, new_medication)—the protocol resolves the patient_id to a specific institution's gateway. This requires a federated identity registry that maps anonymized patient tokens to their home institution without revealing cross-institutional patient lists.

A mechanism that tracks and limits the cumulative information leakage from repeated tool queries. Each invocation of a local database or computational tool consumes a fraction of a pre-defined privacy budget (ε) . The controller applies calibrated noise to query results and may reject requests when the budget is exhausted, preventing adversaries from reconstructing private patient data through statistical analysis of multiple LLM-generated answers.

Each hospital maintains a local tool registry that describes the capabilities, input schemas, and output formats of its available computational resources—such as a drug interaction checker, a dosing calculator, or a clinical trial matcher. A lightweight schema synchronization service periodically pushes anonymized tool definitions to the central LLM's tool catalog, enabling the model to generate correct function calls without ever seeing the underlying implementation or data.

An immutable, append-only log that records every tool invocation across the federated network, including the requesting LLM prompt hash, the tool called, the timestamp, and a cryptographic commitment to the result. This ledger—often implemented on a permissioned blockchain or distributed ledger—provides compliance officers with a tamper-proof audit trail for HIPAA and GDPR requirements without exposing the actual patient data that was processed.

A post-processing module that attaches verifiable provenance to every piece of information the LLM incorporates from a federated tool. When the model cites a drug interaction warning, the engine appends a cryptographically signed attribution token that links back to the specific hospital's tool invocation ledger entry. This allows downstream clinicians to verify the source and recency of the clinical knowledge without accessing the originating hospital's systems.

The foundational architecture that enables a central LLM to query distributed, local vector stores at each hospital. Instead of centralizing patient data, the model sends an encrypted query; the local RAG system retrieves relevant clinical context from private documents and returns only the necessary text chunks. This grounds the LLM's output in institution-specific, verifiable data without exposing the underlying records.

A protocol extension where a central model generates structured tool invocation requests that are routed to and executed within a hospital's secure perimeter. The LLM does not directly call an external API; it emits a signed instruction for a local executor to run a validated clinical calculator, such as CHA₂DS₂-VASc or HAS-BLED, and return only the computed result. This prevents patient-specific data from leaving the institution.

A hardware-based trust mechanism ensuring that when a federated tool is invoked, the computation occurs within a trusted execution environment (TEE) . This provides cryptographic proof that the local drug interaction checker or dosing calculator ran the exact, unmodified code on the specific input, preventing a compromised hospital node from returning falsified results to the central LLM.

A mathematical technique applied to the outputs of local tools before they are returned to the central LLM. By adding carefully calibrated statistical noise to a result—such as a lab value or a risk score—the system provides plausible deniability about any single patient's data. This protects against membership inference attacks that could deduce if a specific individual was in the local database.

A cross-institutional validation layer that uses tool outputs to verify the factual accuracy of the LLM's generated text. When the model proposes a diagnosis or treatment, the system automatically cross-references it against the structured, deterministic results from federated tools like clinical decision support systems. Any contradiction triggers a suppression or revision of the hallucinated statement.

Programmable safety constraints deployed at each hospital node that intercept and validate all tool invocations. These guardrails enforce clinical policy by blocking unsafe or non-compliant requests before execution. For example, a guardrail can prevent a central LLM from requesting a pediatric dosage calculation if the patient context indicates an adult, ensuring the federated tool interaction remains clinically safe.