Federated Hallucination Mitigation

A decentralized evaluation protocol where each institution computes a factuality score on its local validation set without exposing patient data. These scores are securely aggregated to create a global metric.

• Local Metric Computation: Each site calculates precision, recall, and F1 scores against verified clinical ground truth.
• Secure Aggregation: Scores are combined using Federated Averaging or secure multi-party computation to prevent inference of local data quality.
• Drift Detection: Monitors the global factuality score over training rounds to detect when the model begins to hallucinate due to catastrophic forgetting or data distribution shift.

A technique that traces a model's generated clinical statement back to its source evidence within a distributed network of private vector stores, ensuring every claim is grounded.

• Distributed Evidence Retrieval: A central model queries local Federated RAG indexes to find supporting passages for a generated claim.
• Entailment Scoring: A Natural Language Inference (NLI) model runs locally at each site to verify if the retrieved evidence logically entails the generated statement.
• Consensus Mechanism: Attribution scores from multiple sites are aggregated; a claim is flagged as unsubstantiated if no institution's data supports it.

Methods for computing a model's predictive confidence in a decentralized setting, allowing the system to flag high-uncertainty outputs that are likely hallucinations.

• Federated MC Dropout: Multiple stochastic forward passes are performed with dropout enabled at each node, and the variance of predictions is aggregated to estimate epistemic uncertainty.
• Deep Ensembles: Independently trained models from different sites form an ensemble; high disagreement among their predictions signals low confidence.
• Conformal Prediction: A distribution-free framework that produces prediction sets with a guaranteed coverage probability, calibrated across the federated network without sharing raw calibration data.

Programmable, cross-institutional constraints that intercept and validate a foundation model's output before it reaches a clinician, preventing harmful hallucinations.

• NeMo Guardrails Integration: Deploying a shared set of canonical forms and fact-checking flows that run locally at each hospital.
• Drug Interaction Verification: A guardrail that queries a federated drug database to verify that a generated prescription does not conflict with a patient's existing medications.
• Anatomical Impossibility Filters: Rules that block outputs containing physically impossible anatomical claims, such as a procedure on an organ that has been surgically removed.

A decentralized alignment process where clinical feedback on model outputs is collected from distributed practitioners to train a shared reward model that penalizes hallucinations.

• Distributed Feedback Collection: Clinicians at each site rank model responses based on factual accuracy and clinical utility.
• Reward Model Aggregation: A shared Bradley-Terry preference model is trained by aggregating encrypted gradient updates from each institution's feedback data.
• Proximal Policy Optimization (PPO): The global language model is fine-tuned using the aggregated reward model to maximize truthfulness while maintaining medical relevance.

A technique that collaboratively refines multi-step reasoning prompts to ensure a shared model's clinical rationale is logically sound and free of fabricated steps.

• Step-by-Step Auditing: Each reasoning step in a Chain-of-Thought (CoT) output is verified against local knowledge bases at each institution.
• Federated Prompt Optimization: Institutions share only the performance metrics of different CoT prompts, allowing a central optimizer to evolve prompts that minimize logical errors without exposing the prompts' test cases.
• Contradiction Detection: A dedicated model at each site checks for internal contradictions within the generated reasoning chain, flagging inconsistencies for human review.

A set of programmable, safety-critical constraints deployed across the network. These are lightweight, shared rule sets that run locally at each inference point to block non-compliant or clinically dangerous outputs before they reach a user.

• Canonical Rule Sets: A shared library of forbidden terms, logical contradictions, and safety checks.
• Local Execution: Guardrails run on the edge, ensuring no raw data leaves the institution during the safety check.
• Federated Updates: New safety rules discovered at one site can be proposed and federated to all other nodes to instantly protect the entire network.

Techniques to quantify a model's confidence in its own predictions across a decentralized network. High uncertainty is a strong signal of a potential hallucination, flagging the output for human review.

• Federated Deep Ensembles: Multiple models trained at different sites provide a distribution of predictions; high variance indicates uncertainty.
• Federated MC Dropout: Applying dropout at inference time across nodes to generate a probability distribution for each token.
• Clinical Triage: Outputs with high epistemic uncertainty are automatically routed to a human specialist for verification before being actioned.

A decentralized alignment process where clinical feedback is the corrective signal. Practitioners at different institutions rate model outputs for truthfulness and safety. This feedback is aggregated to train a shared reward model that fine-tunes the foundation model to prefer factual responses.

• Distributed Feedback Loop: Clinicians flag hallucinations locally.
• Privacy-Preserving Reward: Only the preference data (e.g., 'Response A is more factual than B') is shared to train the global reward model.
• Continuous Alignment: The model iteratively improves its factuality based on real-world clinical use across the entire network.

A technique to create a smaller, more robust student model that is less prone to hallucination. A large, complex 'teacher' model's outputs on a public dataset are used to train a compact 'student' model. In a federated setting, only the teacher's output logits are shared, not the sensitive data.

• Logit-Based Transfer: The student learns to mimic the teacher's probability distribution, which contains richer information than hard labels.
• Regularization Effect: Distillation often smooths the decision boundary, reducing the model's tendency to make overconfident, incorrect predictions.
• Efficient Deployment: The resulting smaller model can be deployed directly on hospital edge devices for low-latency, private inference.