TL;DR: We study the privacy risks of partial model merging and show that private model components can still be substantially reconstructed under realistic clone-attack settings. 

TL;DR: A metric using limited unlabeled data to measure how well models can be merged by comparing merged weights vs ensemble outputs at layer/node levels. It guides merging and pruning and improves alignment with ensembling.