HBIpFL: Hypernetwork and Backdoor Isolation Personalized Federated Learning

Federated Learning (FL) transforms collaborative machine learning by enabling data privacy-preserving model training across dispersed devices. Unfortunately, several challenges need to be concerned, such as the non-ID features of cross-client data and the possibility of backdoor attacks, which can result in inconsistent models, inefficiency, and security issues. To address these issues, we propose hypernetwork-based personalization and poisoning-free federated learning (HBIpFL), a unique architecture to improve security and personalization in FL. HBIpFL dramatically reduces communication overhead without sacrificing performance by using a hypernetwork-based parameter classifier to dynamically analyze and only upload the most important model parameters. Furthermore, it utilizes Local Gradient Ascent (LGA) methods to track training loss trends and identify possible backdoor intrusions, guaranteeing the resilience and dependability of the global model. We then compare HBIpFL to the state-of-the-art methods in the context of accuracy, communication efficiency, and defense against adversarial attacks. The results demonstrate that our HBIpFL offers a secure and effective FL environment for practical scenarios with a wide range of data distributions and strict privacy specifications.