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arXiv Machine Learning By Halil \.Ibrahim Kanpak, Sinem Sav, Alptekin K\"up\c{c}\"u

HE-OFT: Privacy-Preserving One-Shot Federated Fine-Tuning under Homomorphic Encryption

Read the original on arXiv Machine Learning →

HE‑OFT is a cryptographically secure one‑shot federated fine‑tuning protocol that protects privacy by exchanging only one encrypted contribution and never revealing the trained model. Each client fine‑tunes a low‑rank adapter and a classifier head on a frozen public backbone, keeps the adapter, and uploads an encrypted head displacement that the server combines using multiparty CKKS without decryption. On four text classification tasks and one vision task, HE‑OFT achieves 61–79 % accuracy, retaining 85–96 % of the accuracy of a disclosed model, while a test‑time query takes 443.1–1713.1 s on one core or 56.1–255.1 s on a GPU, with level restoration reducing traffic from 1.6 GiB to 13.5 MiB.

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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