Codes for CPL attacks
This is the prototype code for ESORICS 2020 A Framework for Evaluating Gradient Leakage Attacks in Federated Learning. The talk can be found here: talk.
Examples
ours | DLG | |
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MNIST | ![]() |
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CIFAR10 | ![]() |
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LFW | ![]() |
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Here is a brief description of each file.
-LFW_Deep_Leakage_from_Gradients.ipynb: lfw implementation for DLG attack in (NIPS2019) “Deep leakage from gradients.”
-LFW_enhanced_random_ASR.ipynb: CPL attack with geometric initialization
-LFW_batch.ipynb: CPL attack in batch
-LFW128_enhanced_random_ASR.ipynb: CPL attack with resolution 128*128. Same applies to LFW64_enhanced_random_ASR.ipynb
-LFW_defense.ipynb: CPL attack under high-pass filter and additive noise
If you use our code, please cite:
Wei, Wenqi, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu. "A Framework for Evaluating Client Privacy Leakages in Federated Learning." In European Symposium on Research in Computer Security, pp. 545-566. Springer, Cham, 2020.