Domain Discrepancy Aware Distillation for Model Aggregation in Federated Learning. (arXiv:2210.02190v1 [cs.LG])

Knowledge distillation has recently become popular as a method of model
aggregation on the server for federated learning. It is generally assumed that
there are abundant public unlabeled data on the server. However, in reality,
there exists a domain discrepancy between the datasets of the server domain and
a client domain, which limits the performance of knowledge distillation. How to
improve the aggregation under such a domain discrepancy setting is still an
open problem. In this paper, we first analyze …

aggregation arxiv distillation federated learning