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FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning

H. Fereidooni, A. Pegoraro, P. Rieger, A. Dmitrienko, and A. Sadeghi. To appear soon at the Network and Distributed System Security Symposium (NDSS), (2024)

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Thomas Phillip

Phillip Thiebes

Phillip Schley

Phillip Würzinger

Phillip Inderhees

Other publications of authors with the same name

FLAME: Taming Backdoors in Federated Learning.T. Nguyen, P. Rieger, H. Chen, H. Yalame, H. Möllering, H. Fereidooni, S. Marchal, M. Miettinen, A. Mirhoseini, S. Zeitouni and 3 other author(s). USENIX Security Symposium, page 1415-1432. USENIX Association, (2022)AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms.H. Fereidooni, J. König, P. Rieger, M. Chilese, B. Gökbakan, M. Finke, A. Dmitrienko, and A. Sadeghi. NDSS, The Internet Society, (2023)DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection.P. Rieger, T. Nguyen, M. Miettinen, and A. Sadeghi. NDSS, The Internet Society, (2022)BayBFed: Bayesian Backdoor Defense for Federated Learning.K. Kumari, P. Rieger, H. Fereidooni, M. Jadliwala, and A. Sadeghi. SP, page 737-754. IEEE, (2023)FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning.H. Fereidooni, A. Pegoraro, P. Rieger, A. Dmitrienko, and A. Sadeghi. CoRR, (2023)FLGUARD: Secure and Private Federated Learning.T. Nguyen, P. Rieger, H. Yalame, H. Möllering, H. Fereidooni, S. Marchal, M. Miettinen, A. Mirhoseini, A. Sadeghi, T. Schneider and 1 other author(s). IACR Cryptol. ePrint Arch., (2021)ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks.P. Rieger, M. Chilese, R. Mohamed, M. Miettinen, H. Fereidooni, and A. Sadeghi. USENIX Security Symposium, page 4301-4318. USENIX Association, (2023)SAFELearn: Secure Aggregation for private FEderated Learning.H. Fereidooni, S. Marchal, M. Miettinen, A. Mirhoseini, H. Möllering, T. Nguyen, P. Rieger, A. Sadeghi, T. Schneider, H. Yalame and 1 other author(s). SP (Workshops), page 56-62. IEEE, (2021)Don't Buy the Pig in a Poke: Benchmarking DNNs Inference Performance before Development.C. Völter, T. Koppe, and P. Rieger. HICSS, page 7790-7799. ScholarSpace, (2024)FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks.J. Castillo, P. Rieger, H. Fereidooni, Q. Chen, and A. Sadeghi. ACSAC, page 647-661. ACM, (2023)

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Disambiguation of "Rieger, Phillip"

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FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning

Please choose a person to relate this publication to

Thomas Phillip

Phillip Thiebes

Phillip Schley

Phillip Würzinger

Phillip Inderhees

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Rieger, Phillip"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning

Please choose a person to relate this publication to

Thomas Phillip

Phillip Thiebes

Phillip Schley

Phillip Würzinger

Phillip Inderhees

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
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FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning