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Trusted Aggregation (TAG): Model Filtering Backdoor Defense In Federated Learning
Joseph Lavond · Minhao Cheng · Yao Li

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in Federated Learning: Recent Advances and New Challenges »
Event URL: https://openreview.net/forum?id=3tUku0Nf9gw »

Federated Learning is a framework for training machine learning models from multiple local data sets without access to the data. A shared model is jointly learned through an interactive process between server and clients that combines locally learned model gradients or weights. However, the lack of data transparency naturally raises concerns about model security. Recently, several state-of-the-art backdoor attacks have been proposed, which achieve high attack success rates while simultaneously being difficult to detect, leading to compromised federated learning models. In this paper, motivated by differences in the output layer distribution between models trained with and without the presence of backdoor attacks, we propose a defense method that can prevent backdoor attacks from influencing the model while maintaining the accuracy of the original classification task.

Author Information

Joseph Lavond (University of North Carolina at Chapel Hill)
Minhao Cheng (Hong Kong University of Science and Technology)
Yao Li (University of North Carolina at Chapel Hill)
Yao Li

I am an assistant professor of Statistics at UNC Chapel Hill. I was a Ph.D. student at UC Davis working with Prof. Cho-Jui Hsieh and Prof. Thomas C.M. Lee. I received my master degree in London School of Economics and Political Science under supervision of Prof. Piotr Fryzlewicz. My research focuses on developing new algorithms to resolve the real-world difficulties in the machine learning pipeline. I study both statistical and computational aspects of machine learning models. I am interested in developing new models with statistical guarantees, such as recommeder systems, factorial machine and fiducial inference. Currently, I am working on adversarial examples, trying to improve the robustness of deep neural networks.

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