Temporal Gradient Inversion for Private Trajectory Reconstruction in Embodied Reinforcement Learning

A 24 Sep 2026 arXiv paper introduces TRACE (Temporal Reconstruction Attack on Consecutive Encodings), an amortized temporal gradient-inversion attack that autoregressively reconstructs private observation-action trajectories from per-step policy-learning gradients in distributed embodied reinforcement learning. On held-out embodied scenes, TRACE reaches 18.8 dB PSNR with near-perfect action recovery at 3–4.5 ms per reconstructed frame, outperforming the learning-based baseline on all reconstruction metrics and running orders of magnitude faster than optimization attacks. The authors report the attack generalizes across recurrent, residual and compact transformer victim architectures, multi-modal inputs and larger discrete action spaces, and conclude that protecting temporal gradient streams may require sequence-aware privacy mechanisms.

Computer Science Machine Learning Submitted on 24 Sep 2026 Title:Temporal Gradient Inversion for Private Trajectory Reconstruction in Embodied Reinforcement Learning View PDF http://arxiv.org/pdf/2609.30258v1 HTML experimental https://arxiv.org/html/2609.30258v1 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 autoregressively reconstructs the sequence of private observation-action trajectories from per-step policy-learning gradients. The attack exploits two structural signals ignored by prior single-frame methods: i cross-time correlation between successive embodied gradients, which we formalize via a conditional mutual-information bound, and ii closed-form action recovery from policy-head gradient structure, which we prove exact when standard entropy regularization is sufficiently small. On held-out embodied scenes, TRACE reaches $18.8$ dB PSNR with near-perfect action recovery at $3$-$4.5$ ms per reconstructed frame, dominating the learning-based baseline across all reconstruction metrics and exceeding optimization attacks while running orders of magnitude faster. Further evaluation demonstrates TRACE's broader applicability across recurrent, residual, and compact transformer victim architectures, multi-modal inputs, and larger discrete action spaces. Defense experiments suggest that protecting temporal gradient streams may require sequence-aware privacy mechanisms. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender IArxiv Recommender What is IArxiv? https://iarxiv.org/about arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .