Temporal Gradient Inversion for Private Trajectory Reconstruction in Embodied Reinforcement Learning

Paper recorded by Signals 4 on 2026-09-24 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-24 on arXiv · recorded by Signals 4 on 2026-09-25

Category: cs.LG · 机器学习 · first seen 2026-09-25

Abstract

Distributed learning in embodied reinforcement-learning agents offers a degree of privacy by retaining raw sensor data on-device and transmitting only policy gradients to the server. Yet temporal structure can amplify this leakage beyond single-frame attacks. We introduce Temporal Reconstruction Attack on Consecutive Encodings (TRACE), an amortized temporal gradient-inversion attack that autoregre

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