4 papers
LLM-Attack Suite: A Unified Red-Team Framework for Privacy Evaluation of Split and Distributed LLM Systems
Georgios Politis, Evangelos Pappas
Split language models keep input text on a client but send intermediate representations to a server. Attacks can reconstruct text from these messages. Comparing privacy evaluations…
What Gradients Add to Text Leakage in Split Language Models, Counted per Token and per Document
Georgios Politis, Evangelos Pappas
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 o…
Privacy Failure in Split-LLM Training, The Returned Gradient Nullifies the Decoys
Georgios Politis, Evangelos Pappas
We present a systems-security case study of a two-node split-LLM training system whose privacy evaluation passed while leaving an observable channel untested. The Trusted Local Nod…
Incentivizing Permissionless Distributed Learning of LLMs
Joel Lidin, Amir Sarfi, Evangelos Pappas +3
We describe an incentive system for distributed deep learning of foundational models where peers are rewarded for contributions. The incentive system, \textit{Gauntlet}, has been d…