Communicational and Computational Efficient Federated Domain Adaptation

Abstract

The emerging paradigm of Federated Learning enables mobile users to collaboratively train a model without disclosing their privacy-sensitive data. Nevertheless, data collected from different mobile users may not be independent and identically distributed. Thus directly applying the trained model to a new mobile user usually leads to performance degradation due to the so-called domain shift. Unsupervised Domain Adaptation is an effective technique to mitigate domain shift and transfer knowledge from labeled source domains to the unlabeled target domain. In this article, we design a Federated Domain Adaptation framework that extends Domain Adaptation with the constraints of Federated Learning to train a model for the target domain and preserve the data privacy of all the source and target domains. As mobile devices usually have limited computation and communication capabilities, we design a set of optimization methods that significantly enhance our framework’s computation and communication efficiency, making it more friendly to resource-constrained edge devices. Evaluation results on three datasets show that our framework has comparable performance with the standard centralized training approach, and the optimization methods can reduce the computation and communication overheads by up to two orders of magnitude.