4 papers
Blind Backdoors in Deep Learning Models
Eugene Bagdasaryan, Vitaly Shmatikov
We investigate a new method for injecting backdoors into machine learning models, based on compromising the loss-value computation in the model-training code. We use it to demonstr…
Policy-Based Federated Learning
Kleomenis Katevas, Eugene Bagdasaryan, Jason Waterman +4
In this paper we present PoliFL, a decentralized, edge-based framework that supports heterogeneous privacy policies for federated learning. We evaluate our system on three use case…
Differential Privacy Has Disparate Impact on Model Accuracy
Eugene Bagdasaryan, Vitaly Shmatikov
Differential privacy (DP) is a popular mechanism for training machine learning models with bounded leakage about the presence of specific points in the training data. The cost of d…
How To Backdoor Federated Learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua +2
Federated learning enables thousands of participants to construct a deep learning model without sharing their private training data with each other. For example, multiple smartphon…