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Federated Learning (FL) has recently emerged as a possible way to tackle thedomain shift in real-world Semantic Segmentation (SS) without compromising theprivate nature of the collected data. However, most of the existing works on FLunrealistically assume labeled data in the remote clients. Here we propose anovel task (FFREEDA) in which the clients' data is unlabeled and the serveraccesses a source labeled dataset for pre-training only. To solve FFREEDA, wepropose LADD, which leverages the knowledge of the pre-trained model byemploying self-supervision with ad-hoc regularization techniques for localtraining and introducing a novel federated clustered aggregation scheme basedon the clients' style. Our experiments show that our algorithm is able toefficiently tackle the new task outperforming existing approaches. The code isavailable at https://github.com/Erosinho13/LADD.

Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning