Federated learning (FL) enables privacy-preserving multi-site clinical research, but orchestrating, monitoring, and interpreting federated analyses remains out of reach for the biostatisticians and clinicians who most need them. We present Starfish-FL, a multi-tier agent harness for cross-silo healthcare analytics buil...
Yunkai Bao, Zainab Saad, Farhan Abbas et al.· ACM Transactions on Computin...· 1 citation
This work proposes test-aware mutant generation, in which an LLM receives the problem statement, canonical solution and base tests in a single prompt, and must generate a nontrivial mutant that passes the base unit tests.
Nils Kiele, Zainab Saad, Zi-Rui Wang et al.· 0 citations
FL-MAESTRO is proposed, a multi-agent orchestrator that makes the joint runtime FL decision directly through three specialist LLM agents, one per decision dimension, and matches the accuracy of the strongest energy-aware baseline while cutting wasted round energy from over a third to near zero.
Jia-Jun Wu, Zi-Rui Wang, Jia-Yu Zhou et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
