BatFL: Backdoor Detection on Federated Learning in e-Health

Federated Learning (FL) has received significant interest both from the research field and industry perspective. One of the most promising cross-silo applications on FL is electronic health records mining which trains a model on siloed data. In this application, clients can be different hospitals or health centers that are located in geo-distributed data centers. A central orchestration server (superior health center) organizes the training, while never seeing patients’ raw data. In this paper, we demonstrate that any local hospital in such a collaborative training framework can introduce hidden backdoor functionality into the joint global model. The backdoored joint global model will produce an adversary-expected output when a predefined trigger is attached to its input but it will behave normally for clean inputs. This vulnerability is exacerbated by the distributed nature of FL, making detecting backdoor attacks on FL a challenging work. Based on the coalitional game and Shapley value, we propose an effective and real-time backdoor detection system on FL. Extensive experiments over two machine learning tasks show that our techniques achieve high accuracy and are robust against multi-attackers settings.