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This work addresses leakage through a learned obfuscate-and-recover scheme that protects participants' private datasets while still allowing an independently deployable model to be trained on the server side, making split-based federated LLM fine-tuning practically viable.
It is argued that anomaly detection for agentic AI must reason at the workflow level, where global execution structure exposes signals that local checks cannot see, and presents Skynet, a principled workflow-level anomaly detection framework that turns observed multi-agent execution into directed workflow graphs and sc...
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