From post

Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective.

, , , , , и . CVPR, стр. 9311-9319. Computer Vision Foundation / IEEE, (2021)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed.

 

Другие публикации лиц с тем же именем

APOLLO: An Automated Power Modeling Framework for Runtime Power Introspection in High-Volume Commercial Microprocessors., , , , , , , , , и . MICRO, стр. 1-14. ACM, (2021)AI-Powered IoT System at the Edge., , , , , , , и . CogMI, стр. 242-251. IEEE, (2021)Fisher-aware Quantization for DETR Detectors with Critical-category Objectives., , , , , , , , и . CoRR, (2024)CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification., , , , , и . DAC, стр. 1-6. IEEE, (2023)Towards Efficient and Robust Deep Neural Network Models.. Duke University, Durham, NC, USA, (2022)base-search.net (ftdukeunivdsp:oai:localhost:10161/25173).On Building Efficient and Robust Neural Network Designs., , , , и . IEEECONF, стр. 317-321. IEEE, (2022)SPN dash: fast detection of adversarial attacks on mobile via sensor pattern noise fingerprinting., , , , , и . ICCAD, стр. 132. ACM, (2018)Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective., , , , , и . CVPR, стр. 9311-9319. Computer Vision Foundation / IEEE, (2021)Global Vision Transformer Pruning with Hessian-Aware Saliency., , , , , и . CVPR, стр. 18547-18557. IEEE, (2023)AdverQuil: an efficient adversarial detection and alleviation technique for black-box neuromorphic computing systems., , , , , и . ASP-DAC, стр. 518-525. ACM, (2019)