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Given the rapidly changing machine learning environments and expensive datalabeling, semi-supervised domain adaptation (SSDA) is imperative when thelabeled data from the source domain is statistically different from thepartially labeled data from the target domain. Most prior SSDA research iscentrally performed, requiring access to both source and target data. However,data in many fields nowadays is generated by distributed end devices. Due toprivacy concerns, the data might be locally stored and cannot be shared,resulting in the ineffectiveness of existing SSDA research. This paper proposesan innovative approach to achieve SSDA over multiple distributed andconfidential datasets, named by Federated Semi-Supervised Domain Adaptation(FSSDA). FSSDA integrates SSDA with federated learning based on strategicallydesigned knowledge distillation techniques, whose efficiency is improved byperforming source and target training in parallel. Moreover, FSSDA controls theamount of knowledge transferred across domains by properly selecting a keyparameter, i.e., the imitation parameter. Further, the proposed FSSDA can beeffectively generalized to multi-source domain adaptation scenarios. Extensiveexperiments are conducted to demonstrate the effectiveness and efficiency ofFSSDA design.

Federated Semi-Supervised Domain Adaptation via Knowledge Transfer