Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks
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In this paper, we design an efficient distributed iterative learning method based on support vector machines (SVMs), which tackles federated classification and regression. The proposed method supports efficient computations and model exchange in a network of heterogeneous nodes and allows personalization of the learning model in the presence of non-i.i.d. data. To further enhance privacy, we introduce a random mask procedure that helps avoid data inversion. Finally, we analyze the impact of the proposed privacy mechanisms and the heterogeneity of participant hardware and data on the system performance.
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