In dermatological disease diagnosis, the private data collected by mobile dermatology assistants exist on distributed mobile devices of patients. Federated learning (FL) can use decentralized data to train models while keeping data local. Existing FL methods assume all the data have labels. However, medical data often comes without full labels due to high labeling costs. Self-supervised learning (SSL) methods, contrastive learning (CL) and masked autoencoders (MAE), can leverage the unlabeled data to pre-train models, followed by fine-tuning with limited labels. However, combining SSL and FL has unique challenges. For example, CL requires diverse data but each device only has limited data. For MAE, while Vision Transformer (ViT) based MAE has higher accuracy over CNNs in centralized learning, MAE's performance in FL with unlabeled data has not been investigated. Besides, the ViT synchronization between the server and clients is different from traditional CNNs. Therefore, special synchronization methods need to be designed. In this work, we propose two federated self-supervised learning frameworks for dermatological disease diagnosis with limited labels. The first one features lower computation costs, suitable for mobile devices. The second one features high accuracy and fits high-performance servers. Based on CL, we proposed federated contrastive learning with feature sharing (FedCLF). Features are shared for diverse contrastive information without sharing raw data for privacy. Based on MAE, we proposed FedMAE. Knowledge split separates the global and local knowledge learned from each client. Only global knowledge is aggregated for higher generalization performance. Experiments on dermatological disease datasets show superior accuracy of the proposed frameworks over state-of-the-arts.

翻译：在皮肤病诊断中,移动皮肤病助理收集的私人数据存在于分布式病人的移动设备上。 联邦学习( FL) 可以使用分散化的数据来培训模型, 同时又保持数据本地化。 现有的 FL 方法假定所有数据都有标签。 但是, 医疗数据往往没有完全标签。 由于标签成本高, 医疗数据往往没有完全标签。 自我监督学习( SSL) 方法、 对比学习( CL) 和蒙面的自动解析器( MAE ), 可以将未标记的数据用于预示的隐私模型, 并随后以有限的标签进行微调。 但是, 将 SSL 和 FL 合并起来, 有独特的挑战。 例如, CL 需要不同的数据, 但每个设备只有有限的数据。 MAE 。 在集中学习中, MAE 的视觉变换( VIT) 性能与未贴标签的FL 。 此外, 服务器和客户之间的 VIT 与传统CNN 不同。 因此, 需要设计特殊的交互同步方法。 在这项工作中, 我们建议两个更精确的自我缩的自我监督的自我监督的自我监督的系统共享的系统, 。