The use of machine learning algorithms for anomaly detection has yielded impressive results, leading to significant developments in domains such as healthcare and finance. It is evident that machine learning algorithms require large amounts of data for training, making collaboration essential to obtain sufficient data. However, with such collaboration, the need for privacy whether for individuals or groups has become increasingly important. Numerous instances highlight the negative and potentially dangerous consequences of failing to maintain privacy. Federated learning and privacy-preserving distributed learning techniques ensure privacy when multiple clients or groups collaborate. This paper introduces a novel privacy-preserving distributed learning framework for anomaly detection, integrating the quorum consensus protocol from distributed systems. This framework is model-agnostic in the sense that it can be applied to any machine learning anomaly detection model that outputs anomaly score. For proof of concept, the Isolation Forest algorithm is employed to detect local anomalies at each client. Empirical evaluations demonstrate that both the equal-weighted and weighted quorum-based global models consistently achieve higher F1 scores compared to individual local models across diverse client configurations. This work presents an effective approach to enhance anomaly detection capabilities in multi-client environments while robustly preserving data privacy.