Federated Foundation Models for Healthcare Diagnostics
Latika Chawla, Anurag Shrivastava, Mohammed I. Habelalmateen, Himanshu Shekhar, Praveen Mittal, Shubham Sharma
Abstract
The paper describes the design space, technical possibilities and practical issues of Federated Foundation Models (FFMs) with the aim of performing privacy-preserving, multi-modal, diagnostics of healthcare. We propose that, given that foundation models (large pre-trained multimodal models) and federated learning paradigms converge, an ability to do powerful cross-institutional clinical reasoning on heterogeneous data sources (medical images, clinical notes, time-series vitals, and structured EHR) and retain patient data at home will be possible. FFMs are also argued to be more sample efficient, have good zero- and few-shot generalization to infrequent conditions and modality-agnostic transfer learning which may be obtained even with no site-specific labels. However, realizing these benefits requires addressing privacy leakage in shared updates, distributional heterogeneity across sites, communication and compute constraints, clinically meaningful uncertainty estimation, and regulatory and auditability requirements. We propose a conceptual FFM architecture that combines (i) foundation-model pretraining on large de-identified multimodal corpora, (ii) privacy enhancing technologies (differential privacy, secure aggregation, and homomorphic/SMPCprimitives) during federated adaptation, and (iii) modular finetuning with clinically informed alignment and uncertainty calibration. We also outline a research agenda and evaluation protocol—benchmarks, privacy/utility trade-off metrics, and stress tests on cross-institutional robustness—necessary to credibly assess FFMs for deployment. Our synthesis highlights that while FFMs can materially advance automated, multimodal diagnostics, their safe clinical use depends on systematic privacy guarantees, interpretability, and institution-level validation.