Learning across domains and devices: Style-driven source-free domain adaptation in clustered federated learning

Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the collected data. However, most of the existing works on FL unrealistically assume labeled data in the re-mote clients. Here we propose a novel task (FFreeDA) in which the clients' data is unlabeled and the server accesses a source labeled dataset for pre-training only. To solve FFreeDA, we propose LADD, which leverages the knowledge of the pre-trained model by employing self-supervision with ad-hoc regularization techniques for local training and introducing a novel federated clustered aggregation scheme based on the clients' style. Our experiments show that our algorithm is able to efficiently tackle the new task out-performing existing approaches. The code is available at https://github.com/Erosinho13/LADD.