Collaborative prognosis is a technique that is used to enable assets to improve their ability to predict failures by learning from the failures of similar other assets. This is typically made possible by enabling the assets to communicate with each other. The key enabler of current collaborative prognosis techniques is that they require assets to share their sensor data and failure information between each other, which might be a major constraint due to commercial sensitivities, especially when the assets belong to different companies. This study uses federated learning to address this issue and examines whether this technique will enable collaborative prognosis while ensuring sensitive operational data is not shared between organisational boundaries. An example implementation is demonstrated for the prognosis of a simulated turbofan fleet, where federated averaging algorithm is used as an alternative for the data exchange step. Its performance is compared with a conventional collaborative prognosis that involves failure data exchange. The results confirm that federated averaging retains the performance of conventional collaborative prognosis while eliminating the exchange of failure data within assets. This removes a critical hindrance in industrial adoption of collaborative prognosis, thus enhancing the potential of predictive maintenance.