  In this work, we propose a communication-efficient parameterization, FedPara,
for federated learning (FL) to overcome the burdens on frequent model uploads
and downloads. Our method re-parameterizes weight parameters of layers using
low-rank weights followed by the Hadamard product. Compared to the conventional
low-rank parameterization, our FedPara method is not restricted to low-rank
constraints, and thereby it has a far larger capacity. This property enables to
achieve comparable performance while requiring 3 to 10 times lower
communication costs than the model with the original layers, which is not
achievable by the traditional low-rank methods. The efficiency of our method
can be further improved by combining with other efficient FL optimizers. In
addition, we extend our method to a personalized FL application, pFedPara,
which separates parameters into global and local ones. We show that pFedPara
outperforms competing personalized FL methods with more than three times fewer
parameters.
