FLAIR: Federated Learning with Adaptive and Intelligent Reasoning for Client Selection

The widespread adoption of the Internet of Things (IoT) in our technology-driven society raises significant security and privacy concerns, highlighting the need for collaborative Intrusion Detection Systems (IDS) that leverage Deep Learning (DL) methods to detect suspicious network traffic. Using both the computing power and local data available at distributed end devices, Federated Learning (FL) provides a decentralized learning paradigm that preserves data privacy. However, the system heterogeneity and the Independent and Identically Distributed (non-IID) distribution of FL clients' data introduce various challenges that impact training efficiency. To address these issues, we propose an adaptive approach named FLAIR, which leverages a semi-synchronous FL mechanism and a Decision Transformer (DT) to select clients that not only enhance the global model's performance but also reduce communication and computation overhead. DT helps to make client selection decisions by considering both current and historical information, with the model trained offline using various client selection policies. The process begins by generating an offline database, which will be used to train the DT offline. Then, the DT is deployed for online client selection under a semi-synchronous protocol, enabling a fully adaptive FL system. Extensive experiments on IDS datasets show that FLAIR significantly reduces computation and communication time by up to 94% and 93%, respectively, while maintaining comparable classification performance to the baseline models.