Towards General Deep Leakage in Federated Learning

Advances and open problems in federated learning

Original

Kairouz, P.; McMahan, H. B.; Avent, B.; Bellet, A.; Bennis, M.; Bhagoji, A. N.; Bonawitz, K.; Charles, Z.; Cormode, G.; Cummings, R.; et al. 2019 · 1912

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iDLG: Improved Deep Leakage from Gradients

Original

Zhao, B.; Mopuri, K. R.; and Bilen, H. 2020 · 2001

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Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

Fredrikson, M.; Jha, S.; and Ristenpart, T. 2015 · 2015

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Understanding deep image representations by inverting them

Mahendran, A.; and Vedaldi, A. 2015 · 2015

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Federated Learning of Deep Networks using Model Averaging

Original

McMahan, H. B.; Moore, E.; Ramage, D.; and y Arcas, B. A. 2016 · 2016

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Deep Leakage from Gradients

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Inverting Gradients - How easy is it to break privacy in federated learning?

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Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning

Salem, A.; Bhattacharya, A.; Backes, M.; Fritz, M.; and Zhang, Y. 2020 · 2020

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See through Gradients: Image Batch Recovery via GradInversion

Yin, H.; Mallya, A.; Vahdat, A.; Alvarez, J. M.; Kautz, J.; and Molchanov, P. 2021 · 2021

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