Detecting malicious attacks is essential for protecting computer systems and ensuring device security. Federated Learning (FL)-based Intrusion Detection Systems (IDS) have emerged as promising solutions, enabling multiple clients (i.e., data owners) to collaboratively train intrusion detection models without sharing private data. However, current FL studies typically assume that each client’s training and test label distribution is identical. This assumption is overly idealistic and rarely holds in real-world scenarios, leading to suboptimal performance when label distribution shifts occur between the training and testing data. To address this challenge, we propose FedPRO, a plug-and-play framework designed to improve the test-time performance of existing FL methods, without modifying their original training pipelines or fine-tuning the trained FL models. Specifically, we develop a unique prototype generation and optimization mechanism to produce semantically meaningful class prototypes. These prototypes constitute a prototype memory bank, serving as an external knowledge repository. At test time, a prototype retrieval-augmented inference strategy is employed to query relevant prototypes and refine predictions on each client, effectively alleviating the label distribution shift issues and boosting prediction accuracy. We evaluate FedPRO by integrating it with various off-the-shelf FL methods on benchmark datasets. Extensive results consistently demonstrate its effectiveness in diverse settings. Notably, applying FedPRO to the state-of-the art method FedDBE improves its test accuracy from 79.25% to 86.66% on the CICIDS-2018 dataset, while introducing only approximately 32KB of additional communication overhead.