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Nowadays, many problems require learning a model from data owned by different participants who are restricted to share their examples due to privacy concerns, which is referred to as multiparty learning in the literature. In conventional multiparty learning, a global model is usually trained from scratch via a communication protocol, ignoring the fact that each party may already have a local model trained on her own dataset. In this paper, we define a multiparty multiclass margin to measure the global behavior of a set of heterogeneous local models, and propose a general learning method called HMR (Heterogeneous Model Reuse) to optimize the margin. Our method reuses local models to approximate a global model, even when data are non-i.i.d distributed among parties, by exchanging few examples under predefined budget. Experiments on synthetic and real-world data covering different multiparty scenarios show the effectiveness of our proposal.
Author Information
Xi-Zhu Wu (Nanjing University)
Song Liu (University of Bristol)
Zhi-Hua Zhou (Nanjing University)
Related Events (a corresponding poster, oral, or spotlight)
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2019 Oral: Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin »
Tue. Jun 11th 10:15 -- 10:20 PM Room Seaside Ballroom
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