On the Vulnerability of Backdoor Defenses for Federated Learning

Understand

Federated Learning (FL) is a popular distributed machine learning paradigm that enables jointly training a global model without sharing clients' data.

• However, its repetitive server-client communication gives room for backdoor attacks with aim to mislead the global model into a targeted misprediction when a specific trigger pattern is presented.
• In response to such backdoor threats on federated learning, various defense measures have been proposed.
• In this paper, we study whether the current defense mechanisms truly neutralize the backdoor threats from federated learning in a practical setting by proposing a new federated backdoor attack method for possible countermeasures.