  Personalized federated learning (PFL) enables customized models for clients
with varying data distributions. However, existing PFL methods often incur high
computational and communication costs, limiting their practical application.
This paper proposes a novel PFL method, Class-wise Federated Averaging
(cwFedAVG), that performs Federated Averaging (FedAVG) class-wise, creating
multiple global models per class on the server. Each local model integrates
these global models weighted by its estimated local class distribution, derived
from the L2-norms of deep network weights, avoiding privacy violations.
Afterward, each global model does the same with local models using the same
method. We also newly designed Weight Distribution Regularizer (WDR) to further
enhance the accuracy of estimating a local class distribution by minimizing the
Euclidean distance between the class distribution and the weight norms'
distribution. Experimental results demonstrate that cwFedAVG matches or
outperforms several existing PFL methods. Notably, cwFedAVG is conceptually
simple yet computationally efficient as it mitigates the need for extensive
calculation to collaborate between clients by leveraging shared global models.
Visualizations provide insights into how cwFedAVG enables local model
specialization on respective class distributions while global models capture
class-relevant information across clients.
