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Found 2 papers, 1 papers with code
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data
In addition, the ML models trained with DeCaPH framework in general outperform those trained solely with the private datasets from individual parties, showing that DeCaPH enhances the model generalizability.
Proof-of-Learning is Currently More Broken Than You Think
They empirically argued the benefit of this approach by showing how spoofing--computing a proof for a stolen model--is as expensive as obtaining the proof honestly by training the model.
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