GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning

With heightened awareness of data privacy protection, Federated Learning (FL) has attracted widespread attention as a privacy-preserving distributed machine learning method. However, the distributed nature of federated learning also provides opportunities for backdoor attacks, where attackers can guide the model to produce incorrect predictions without affecting the global model training process. This paper introduces a novel defense mechanism against backdoor attacks in federated learning, named GANcrop. This approach leverages contrastive learning to deeply explore the disparities between malicious and benign models for attack identification, followed by the utilization of Generative Adversarial Networks (GAN) to recover backdoor triggers and implement targeted mitigation strategies. Experimental findings demonstrate that GANcrop effectively safeguards against backdoor attacks, particularly in non-IID scenarios, while maintaining satisfactory model accuracy, showcasing its remarkable defensive efficacy and practical utility.

FedDCT: A Dynamic Cross-Tier Federated Learning Scheme in Wireless Communication Networks

With the rapid proliferation of Internet of Things (IoT) devices and the growing concern for data privacy among the public, Federated Learning (FL) has gained significant attention as a privacy-preserving machine learning paradigm. FL enables the training of a global model among clients without exposing local data. However, when a federated learning system runs on wireless communication networks, limited wireless resources, heterogeneity of clients, and network transmission failures affect its performance and accuracy. In this study, we propose a novel dynamic cross-tier FL scheme, named FedDCT to increase training accuracy and performance in wireless communication networks. We utilize a tiering algorithm that dynamically divides clients into different tiers according to specific indicators and assigns specific timeout thresholds to each tier to reduce the training time required. To improve the accuracy of the model without increasing the training time, we introduce a cross-tier client selection algorithm that can effectively select the tiers and participants. Simulation experiments show that our scheme can make the model converge faster and achieve a higher accuracy in wireless communication networks.