Revisiting Data-Free Knowledge Distillation with Poisoned Teachers a. 作者旨在揭示在数据不可用的知识蒸馏(KD)中,不可靠的预训练模型可能带来的安全风险,并提出了一种名为Anti-Backdoor Data-Free KD (ABD)的防御方法来减轻潜在后门被传递的风险。 b. 这件事的难点在于如何在没有原始训练数据的情况下,确保从大...
简单来说,现有的FL聚合器如 FedProx,Scaffold 只通过约束 local client 在本地训练时不过分偏离 global model 来缓解 non-IID 带来的性能下降。无意忽略了 global model 聚合过程对性能的影响。i.e.,以往的 naive 的聚合策略得到的 global model 真的足够好吗?本文将对该问题进行探究。并基于 data-free KD 和...
In this work, we make the first effort to uncover the security risk of data-free KD w.r.t. untrusted pre-trained models. We then propose Anti-Backdoor Data-Free KD (ABD), the first plug-in defensive method for data-free KD methods to mitigate the chance of potential backdoors being ...
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Inspired by recent works in data-free Knowledge Distillation (KD), we train the generative model using a disagreement objective to produce inputs that maximize disagreement between the clone and the target model. However, unlike the white-box setting of KD, where the gradient information is ...
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Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data [1995] OJ L281/31. 24. EU Directive 95/46/EC has a dual nature. It was based on...
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