In this talk, I will introduce recent works on model fusion, a special case of Federated Learning where only a single communication round is allowed. This setting has a unique feature where it is sufficient for clients to have a pre-trained model, but not the data. Data storage regulations such as GDPR make this setting appealing as the data can be immediately deleted after updating the local model before FL starts. In addition to deep learning methods, I will cover unsupervised settings such as mixture models, topic models, and hidden Markov models.