LFighter: Defending against the label-flipping attack in federated learning

Authors:
raw_author_name: Najeeb Moharram Jebreel
raw_affiliation_string: Universitat Rovira i Virgili, Department of Computer Engineering and Mathematics, CYBERCAT Center for Cybersecurity Research of Catalonia, UNESCO Chair in Data Privacy, Av. Països Catalans 26, E-43007 Tarragona, Catalonia
raw_author_name: Josep Domingo-Ferrer
raw_affiliation_string: Universitat Rovira i Virgili, Department of Computer Engineering and Mathematics, CYBERCAT Center for Cybersecurity Research of Catalonia, UNESCO Chair in Data Privacy, Av. Països Catalans 26, E-43007 Tarragona, Catalonia
raw_author_name: David Sánchez
raw_affiliation_string: Universitat Rovira i Virgili, Department of Computer Engineering and Mathematics, CYBERCAT Center for Cybersecurity Research of Catalonia, UNESCO Chair in Data Privacy, Av. Països Catalans 26, E-43007 Tarragona, Catalonia
raw_author_name: Alberto Blanco-Justicia
raw_affiliation_string: Universitat Rovira i Virgili, Department of Computer Engineering and Mathematics, CYBERCAT Center for Cybersecurity Research of Catalonia, UNESCO Chair in Data Privacy, Av. Països Catalans 26, E-43007 Tarragona, Catalonia

Abstract: Federated learning (FL) provides autonomy and privacy by design to participating peers, who cooperatively build a machine learning (ML) model while keeping their private data in their devices. However, that same autonomy opens the door for malicious peers to poison the model by conducting either untargeted or targeted poisoning attacks. The label-flipping (LF) attack is a targeted poisoning attack where the attackers poison their training data by flipping the labels of some examples from one class (i.e., the source class) to another (i.e., the target class). Unfortunately, this attack is easy to perform and hard to detect, and it negatively impacts the performance of the global model. Existing defenses against LF are limited by assumptions on the distribution of the peers' data and/or do not perform well with high-dimensional models. In this paper, we deeply investigate the LF attack behavior. We find that the contradicting objectives of attackers and honest peers on the source class examples are reflected on the parameter gradients corresponding to the neurons of the source and target classes in the output layer. This makes those gradients good discriminative features for the attack detection. Accordingly, we propose LFighter, a novel defense against the LF attack that first dynamically extracts those gradients from the peers' local updates and then clusters the extracted gradients, analyzes the resulting clusters, and filters out potential bad updates before model aggregation. Extensive empirical analysis on three data sets shows the effectiveness of the proposed defense regardless of the data distribution or model dimensionality. Also, LFighter outperforms several state-of-the-art defenses by offering lower test error, higher overall accuracy, higher source class accuracy, lower attack success rate, and higher stability of the source class accuracy. Our code and data are available for reproducibility purposes at https://github.com/NajeebJebreel/LFighter.