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Federated learning is a learning paradigm to enable collaborative learningacross different parties without revealing raw data. Notably, verticalfederated learning (VFL), where parties share the same set of samples but onlyhold partial features, has a wide range of real-world applications. However,most existing studies in VFL disregard the "record linkage" process. Theydesign algorithms either assuming the data from different parties can beexactly linked or simply linking each record with its most similar neighboringrecord. These approaches may fail to capture the key features from other lesssimilar records. Moreover, such improper linkage cannot be corrected bytraining since existing approaches provide no feedback on linkage duringtraining. In this paper, we design a novel coupled training paradigm, FedSim,that integrates one-to-many linkage into the training process. Besides enablingVFL in many real-world applications with fuzzy identifiers, FedSim alsoachieves better performance in traditional VFL tasks. Moreover, wetheoretically analyze the additional privacy risk incurred by sharingsimilarities. Our experiments on eight datasets with various similarity metricsshow that FedSim outperforms other state-of-the-art baselines. The codes ofFedSim are available at https://github.com/Xtra-Computing/FedSim.

A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning