Scientific artificial intelligence (AI), spanning foundation models (FMs) to federated data-analysis pipelines, is becoming shared infrastructure across national laboratories, universities, hospitals, and industrial partners. This collaboration creates privacy risks whose natural unit is often an institution's particip...
O. Kotevska, Sumit Kumar Jha, A. Bellet et al.· 1 citation
In a five-site simulation, the federated meta-analysis reproduces the association signal expected from a pooled analysis without centralizing any genotype data, showing that standards-based task execution and federated learning enables a practical privacy-preserving infrastructure for international GWAS meta-analysis.
Abhijith Chunduru, M. Joel, Zilinghan Li et al.· 0 citations
It is argued that offering federated computing as an important AmSC service would unlock privacy-constrained scientific collaborations, enabling public-private partnerships in model building while exercising and enhancing the platform's own federated infrastructure.
Zilinghan Li, Abhijith Chunduru, H. Krishnan et al.· 0 citations
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