A Path Integral Surrogate for Multi-Step Gradient Inversion in Federated Learning

Paper recorded by Signals 4 on 2026-10-02 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-02 on arXiv · recorded by Signals 4 on 2026-10-05

Category: cs.LG · 机器学习 · first seen 2026-10-05

Abstract

Federated learning lets many clients train a shared model together without ever sending their private data to a central server. Each client shares only a model update, and this update should reveal far less about the client than its raw training examples would. This premise is what protects the privacy of the clients. Gradient inversion attacks challenge it directly by trying to reconstruct a clie

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