  This paper focuses on Federated Domain-Incremental Learning (FDIL) where each
client continues to learn incremental tasks where their domain shifts from each
other. We propose a novel adaptive knowledge matching-based personalized FDIL
approach (pFedDIL) which allows each client to alternatively utilize
appropriate incremental task learning strategy on the correlation with the
knowledge from previous tasks. More specifically, when a new task arrives, each
client first calculates its local correlations with previous tasks. Then, the
client can choose to adopt a new initial model or a previous model with similar
knowledge to train the new task and simultaneously migrate knowledge from
previous tasks based on these correlations. Furthermore, to identify the
correlations between the new task and previous tasks for each client, we
separately employ an auxiliary classifier to each target classification model
and propose sharing partial parameters between the target classification model
and the auxiliary classifier to condense model parameters. We conduct extensive
experiments on several datasets of which results demonstrate that pFedDIL
outperforms state-of-the-art methods by up to 14.35\% in terms of average
accuracy of all tasks.
