Federated Transfer Learning

We have discussed horizontal federated learning (HFL) and vertical federated learning (VFL) in Chapters 4 and 5, respectively. HFL requires all participating parties share the same feature space while VFL require parties share the same sample space. In practice, however, we often face situations in which there are not enough shared features or samples among the participating parties. In those cases, one can still build a federated learning model combined with transfer learning that transfers knowledge among the parties to achieve better performance. We refer to the combination of federated learning and transfer learning as Federated Transfer Learning (FTL). In this chapter, we provide a formal definition of FTL and discuss the differences between FTL and traditional transfer learning. We then introduce a secure FTL framework proposed in Liu et al. [2019], and conclude this chapter with a summary of the challenges and open issues.