Hammer and Anvil framework categorizes backdoors by update deviation δ and shows that principled combinations of Type-1 outlier/robust and Type-2 removal defenses resist full-information adaptive adversaries.
Communication- efficient learning of deep networks from decentralized data
2 Pith papers cite this work. Polarity classification is still indexing.
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An index-aided scheme uses EncGAN to produce random-looking yet ML-usable indices over AES-encrypted data, with small accuracy drops on image classification.
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Hammer and Anvil: Toward a Theory of Backdoors in Federated Learning
Hammer and Anvil framework categorizes backdoors by update deviation δ and shows that principled combinations of Type-1 outlier/robust and Type-2 removal defenses resist full-information adaptive adversaries.
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MLQENABLER: Enabling Secure Machine Learning Queries over Encrypted Database in Cloud Computing
An index-aided scheme uses EncGAN to produce random-looking yet ML-usable indices over AES-encrypted data, with small accuracy drops on image classification.