Author

E. Pappas

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#machine learning Preprint Oct 2026

What Gradients Add to Text Leakage in Split Language Models, Counted per Token and per Document

Split learning lets a client train a language model on a server without sending its text. The client runs the first layers itself and sends the server only their output, a vector of numbers for each token. During training, the server sends gradients back. We show that an observer at the split can rebuild most of the cl...

G. Politis, E. Pappas · 0 citations

#machine learning Preprint Sep 2026

Privacy Failure in Split-LLM Training, The Returned Gradient Nullifies the Decoys

A systems-security case study of a two-node split-LLM training system whose privacy evaluation passed while leaving an observable channel untested, but the system is not thereby safe: five classes of attack, including those accumulating observations across training steps, were never measured.

G. Politis, E. Pappas · 1 citation