Title: Multi-Step Decentralized Domain Adaptation. Abstract: Despite the recent breakthroughs in unsupervised domain adaptation (uDA), no prior work has studied the challenges of applying these methods in practical machine learning scenarios. In this paper, we highlight two significant bottlenecks for uDA, namely excessive centralization and poor support for distributed domain datasets. Our proposed framework, MDDA, is powered by a novel collaborator selection algorithm and an effective distributed adversarial training method, and allows for uDA methods to work in a decentralized and privacy-preserving way.  
