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

FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning