Current technological advancements in Software Defined Networks (SDN) can provide efficient solutions for smart grids (SGs). An SDN-based SG promises to enhance the efficiency, reliability and sustainability of the communication network. However, new security breaches can be introduced with this adaptation. A layer of defence against insider attacks can be established using machine learning based intrusion detection system (IDS) located on the SDN application layer. Conventional centralised practises, violate the user data privacy aspect, thus distributed or collaborative approaches can be adapted so that attacks can be detected and actions can be taken. This paper proposes a new SDN-based SG architecture, highlighting the existence of IDSs in the SDN application layer. We implemented a new smart meter (SM) collaborative intrusion detection system (SM-IDS), by adapting the split learning methodology. Finally, a comparison of a federated learning and split learning neighbourhood area network (NAN) IDS was made. Numerical results showed, a five class classification accuracy of over 80.3% and F1-score 78.9 for a SM-IDS adapting the split learning technique. Also, the split learning NAN-IDS exhibit an accuracy of over 81.1% and F1-score 79.9.