  Personalized Federated Learning (pFL) not only can capture the common priors
from broad range of distributed data, but also support customized models for
heterogeneous clients. Researches over the past few years have applied the
weighted aggregation manner to produce personalized models, where the weights
are determined by calibrating the distance of the entire model parameters or
loss values, and have yet to consider the layer-level impacts to the
aggregation process, leading to lagged model convergence and inadequate
personalization over non-IID datasets. In this paper, we propose a novel pFL
training framework dubbed Layer-wised Personalized Federated learning (pFedLA)
that can discern the importance of each layer from different clients, and thus
is able to optimize the personalized model aggregation for clients with
heterogeneous data. Specifically, we employ a dedicated hypernetwork per client
on the server side, which is trained to identify the mutual contribution
factors at layer granularity. Meanwhile, a parameterized mechanism is
introduced to update the layer-wised aggregation weights to progressively
exploit the inter-user similarity and realize accurate model personalization.
Extensive experiments are conducted over different models and learning tasks,
and we show that the proposed methods achieve significantly higher performance
than state-of-the-art pFL methods.
