UaaS-SFL: Unlearning as a Service for Safeguarding Federated Learning

Abstract

The rapid expansion of the Internet of Things (IoT) and network services has revolutionized technology, enabling numerous intelligent applications. However, this interconnected environment also introduces significant security challenges, particularly the susceptibility of federated learning (FL) systems to poisoning attacks. Such attacks compromise the integrity of the global model by injecting malicious data, leading to inaccurate predictions and potentially endangering system reliability and user safety. While traditional approaches, such as early detection and secure aggregation methods, aim to prevent the aggregation of malicious updates, they are ineffective in addressing threats within systems that have already been compromised and did not initially implement these safeguards. This gap highlights the urgent need for robust post-compromise mitigation strategies in FL security. To address this challenge, we introduce “Unlearning as a Service for Safeguarding Federated Learning” (UaaS-SFL), a novel service designed to seamlessly integrate with any FL management system to remove the impact of poisoning clients and restore the integrity of the global model. UaaS-SFL effectively unlearns the contributions of malicious clients, ensuring both model security and system reliability. Our empirical evaluations, conducted in a simulated IoT environment, demonstrate that our service maintains model accuracy with less than a 10% deviation from the baseline achieved through retraining from scratch, underscoring the efficacy of our methodology in safeguarding FL systems. These results highlight UaaS-SFL as a critical service for securing FL management systems, providing a robust foundation for the continued growth of secure and intelligent IoT applications.