RFLBAT: A Robust Federated Learning Algorithm against Backdoor Attack
This addresses a critical security problem in federated learning for distributed systems like mobile and IoT devices, but it is incremental as it builds on existing defense methods.
The paper tackles the vulnerability of federated learning to backdoor attacks, especially under unknown numbers of malicious attackers and non-IID data distributions, by proposing RFLBAT, which uses PCA and K-means clustering to defend against such attacks and outperforms existing state-of-the-art algorithms in various scenarios.
Federated learning (FL) is a distributed machine learning paradigm where enormous scattered clients (e.g. mobile devices or IoT devices) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. Unfortunately, FL is susceptible to a variety of attacks, including backdoor attack, which is made substantially worse in the presence of malicious attackers. Most of algorithms usually assume that the malicious at tackers no more than benign clients or the data distribution is independent identically distribution (IID). However, no one knows the number of malicious attackers and the data distribution is usually non identically distribution (Non-IID). In this paper, we propose RFLBAT which utilizes principal component analysis (PCA) technique and Kmeans clustering algorithm to defend against backdoor attack. Our algorithm RFLBAT does not bound the number of backdoored attackers and the data distribution, and requires no auxiliary information outside of the learning process. We conduct extensive experiments including a variety of backdoor attack types. Experimental results demonstrate that RFLBAT outperforms the existing state-of-the-art algorithms and is able to resist various backdoor attack scenarios including different number of attackers (DNA), different Non-IID scenarios (DNS), different number of clients (DNC) and distributed backdoor attack (DBA).