Impact of model architecture and data distribution on self-supervised federated learning
Data is a crucial resource for machine learning. But in many settings, such as in healthcare or on mobile devices, there are obstacles that make it difficult to utilize the available data. This data is often distributed between many clients and private, meaning that central storage of the data is inadvisable. Further, image data is often unlabeled and external labelling is impossible due to its pr
