Graph neural networks (GNNs) are an emerging tool for analysing graph-structured data, yet their training and inference require access to large and complex graphs. Major organisations such as Google and Facebook hold massive graph datasets (e.g., user interactions/relationships), but cannot share these directly due to their sensitive nature. We propose a secure framework, ColabGNN, for collaborative GNN training and inference that leverages Function Secret Sharing (FSS) to protect both the structure and features of private graphs. Our method ensures that raw graph data, training/inference computations and client queries remain hidden, while still enabling joint model training and secure inference across distributed parties.