Towards Practical Backdoor Attacks on Federated Learning Systems

Federated Learning (FL) is nowadays one of the most promising paradigms for privacy-preserving distributed learning. Without revealing its local private data to outsiders, a client in FL systems collaborates to build a global Deep Neural Network (DNN) by submitting its local model parameter update to a central server for iterative aggregation. With secure multi-party computation protocols, the submitted update of any client is also by design invisible to the server. Seemingly, this standard design is a win-win for client privacy and service provider utility. Ironically, any attacker may also use manipulated or impersonated client to submit almost any attack payloads under the umbrella of the FL protocol itself. In this work, we craft a practical backdoor attack on FL systems that is proved to be simultaneously effective and stealthy on diverse use cases of FL systems and leading commercial FL platforms in the real world. Basically, we first identify a small number of redundant neurons which tend to be rarely or slightly updated in the model, and then inject backdoor into these redundant neurons instead of the whole model. In this way, our backdoor attack can achieve a high attack success rate with a minor impact on the accuracy of the original task. As countermeasures, we further consider several common technical choices including robust aggregation mechanisms, differential privacy mechanism,s and network pruning. However, none of the defenses show desirable defense capability against our backdoor attack. Our results strongly highlight the vulnerability of existing FL systems against backdoor attacks and the urgent need to develop more effective defense mechanisms.

I. Introduction

As a promising distributed learning paradigm in the era of edge computing, the concept of Federated Learning (FL) was first proposed by Google in 2016 [1], [2], [3]. Intuitively, the main idea of FL is to build deep learning models based on datasets that are distributed over multiple end devices (i.e., clients) in a privacy-preserving way. Recently, FL has already found a wide range of applications in popular mobile apps, such as Google's GBoard [4], Apple's QuickType [5], and various data sensitive application domains including financial [6], medical [7] and security [8] scenarios, where distributed data cannot be directly collected to a central server for training deep learning models, due to various factors such as intellectual property rights, government regulations, and other physical constraints [9].