Gradient Inversion Attacks

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Temporal Gradient Inversion for Private Trajectory Reconstruction in Embodied Reinforcement Learning
Sep 24, 2026Sudip Bhujel, Shanghao Shi, Ruiquan Huang +2Gradient Inversion Attacks Robotic RL
Department of Computer Science, University of Kentucky · Department of Computer Science and Engineering, Washington University in St. Louis
Cascading Gradient Inversion via LT-Code Inspired Peeling in Federated Learning
Sep 10, 2026Saeed Shariati, Mohsen Alambardar MeybodiGradient Inversion Attacks Federated Learning
Department of Applied Mathematics and Computer Science, Faculty of Mathematics and Statistics, University of Isfahan, Isfahan 81746-73441, Iran
MineGrad: Gradient Inversion Attacks on LoRA Fine-Tuning
Aug 2, 2026Hasin Us Sami, Swapneel Sen, Basak GulerGradient Inversion Attacks Privacy Leakage in Language Models
University of California, Riverside, CA
BettiSplit: Topology-Guided Privacy-Aware Split Learning Against Feature Inversion and Gradient Leakage
Jul 27, 2026Akarsh K. Nair, Muhammad Arifur Rahman, David Brown +1Gradient Inversion Attacks Split Federated Learning
Department of Computer Science Nottingham Trent University Nottingham, United Kingdom · Information and Computer Science Department SDAIA–KFUPM Joint Research Center for AI Interdisciplinary Research Center for Biosystems and Machines King Fahd University of Petroleum & Minerals Dhahran 31261, Saudi Arabia
DecoyFace: Beyond Obfuscation via Controllable and Imperceptible Identity Misdirection for Privacy-Preserving Face Recognition
Jul 20, 2026Zhihan Ren, Lijun He, Xinyao Wang +2Gradient Inversion Attacks Privacy-Preserving ML
Shaanxi Key Laboratory of Deep Space Exploration Intelligent Information Technology, School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks
Jul 15, 2026Santhosh Parampottupadam, Andres Martinez, Dimitrios Bounias +3Gradient Inversion Attacks Adversarial Attacks
German Cancer Research Center (DKFZ), Division of Medical Image Computing, Heidelberg, Germany · Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany · Department of Computer Engineering, Bilkent University, Universiteler 06800 Çankaya/Ankara, Türkiye +1
TrustCLIP: Learning Private Visual Features via Adversarial Reconstruction
Jul 5, 2026Nikos Athanasiou, Ilya A. Petrov, Angela Yao +7Gradient Inversion Attacks VLM Robustness
Meta, Zürich, Switzerland · Max Planck Institute for Intelligent Systems, Tübingen, Germany · University of Tübingen, Germany +1
TIGER: Inverting Transformer Gradients via Embedding-Subspace Distance Optimization
Jun 16, 2026William Kalikman, Ivo Petrov, Dimitar I. Dimitrov +1Gradient Inversion Attacks Transformer
ETH Zürich · INSAIT, Sofia University "St. Kliment Ohridski"
Profiling Privacy Preservation Against Gradient Inversion Attacks in Tabular Federated Learning
May 31, 2026Ivo Osterberg Nilsson, Maximilian Birr Engvall, Viktor Valadi +1Gradient Inversion Attacks Adversarial Attacks
Department of Computing, Jönköping University, Jönköping, Sweden · Scaleout Systems, Uppsala, Sweden · Department of Information Systems, University of Haifa, Haifa, Israel
No More Guessing: a Verifiable Gradient Inversion Attack in Federated Learning
Apr 16, 2026Francesco Diana, Chuan Xu, André Nusser +1Gradient Inversion Attacks Adversarial Attacks
Université Côte d’Azur, France · Inria, France · CNRS, France +1
Trajectory-Aware Information Matching for Multi-Step Gradient Inversion in Federated Learning
Sep 26, 2025Li Xia, Jing Yu, Zheng Liu +3Gradient Inversion Attacks Adversarial Attacks
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China
Practical Feasibility of Gradient Inversion Attacks in Federated Learning
Aug 27, 2025Viktor Valadi, Lucas Beerens, Mattias Åkesson +4Gradient Inversion Attacks Adversarial Attacks
Scaleout Systems Sweden · Recorded Future Sweden · AI Sweden Sweden +1
GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning
Dec 17, 2024Rui Zhang, Ka-Ho ChowGradient Inversion Attacks Adversarial Attacks
HKU Musketeers Foundation Institute of Data Science · The University of Hong Kong · Hong Kong SAR, China +1

Sep 24, 2026, cs.LGJ/K move · Enter open · S save

Temporal Gradient Inversion for Private Trajectory Reconstruction in Embodied Reinforcement Learning

Sudip Bhujel, Shanghao Shi, Ruiquan Huang, Ning Zhang+1

Department of Computer Science, University of Kentucky · Department of Computer Science and Engineering, Washington University in St. Louis

Gradient Inversion Attacks Robotic RL

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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.

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