Federated Foundation Model

Enables collaborative fine-tuning of large pre-trained models across multiple hospitals without centralizing Protected Health Information (PHI). Only model updates—never raw patient data—leave the institutional firewall.

• Federated LoRA injects trainable low-rank matrices into frozen foundation model layers, reducing communication overhead by up to 10,000x compared to full model sharing
• Federated Prompt Tuning learns shared soft prompts across institutions without modifying base model weights
• Local data remains sovereign while the global model benefits from diverse clinical populations

Adapts billion-parameter foundation models to clinical domains by training and aggregating only a tiny fraction of adapter parameters. This dramatically reduces bandwidth requirements and enables participation from resource-constrained hospitals.

• Federated QLoRA combines 4-bit quantization with low-rank adaptation for fine-tuning on consumer-grade GPUs
• Federated Adapter Modules insert small bottleneck layers into transformer blocks, training less than 1% of total parameters
• Enables community hospitals with limited compute to contribute to and benefit from cutting-edge medical AI

A decentralized RAG architecture where a central language model queries distributed vector stores at each institution to retrieve clinically relevant context. The model generates grounded, evidence-based responses without ever centralizing the underlying medical records.

• Local vector databases index institutional clinical notes, radiology reports, and research literature
• The central LLM receives only retrieved text chunks, not database access
• Maintains HIPAA compliance while enabling cross-institutional knowledge synthesis

Transfers knowledge from a large global teacher model to smaller, deployable student models at each site without sharing private gradients. The teacher's output logits on public or synthetic datasets serve as the only shared signal.

• Federated Ensemble Distillation compresses knowledge from multiple independently-trained site models into a single robust student
• Protects against model inversion attacks by never exposing individual model parameters
• Enables deployment of compact, specialized models on hospital edge devices

Distributes the critical process of aligning foundation models with clinical best practices across institutions. Federated RLHF aggregates practitioner feedback to train a shared reward model that steers outputs toward medical accuracy and safety.

• Federated Guardrails enforce programmable safety constraints across all nodes
• Federated Hallucination Mitigation uses distributed factuality scoring to reduce false clinical claims
• Clinicians at each site provide domain-specific feedback, creating a model aligned with diverse medical standards

Leverages a foundation model pre-trained on massive public corpora and adapts only the final task-specific layers on private clinical data at each institution. Only these lightweight updates are aggregated centrally.

• Base model weights remain frozen and never exposed to institutional data
• Federated Meta-Learning finds optimal initializations for rapid adaptation to new clinical tasks with minimal local data
• Addresses non-IID data distributions across hospitals with different patient demographics and equipment