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arXiv Machine Learning By Tom Kimpson, Mauricio Baker, Emlyn Graham

Detecting AI Training in Datacentre Power

Read the original on arXiv Machine Learning →

The paper proposes a method to detect large AI model training runs on a data‑centre power grid using Viterbi tracking of iteration cadence. On simulated data, the tracker identifies at least 96 % of training runs with a 1 % false‑alarm rate when the operator does not hide activity, and remains effective even against several adversarial hiding strategies. The approach requires only a modest passive power meter sampling at 2 Hz or faster, making hidden training both detectable and costly for the operator.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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