An Efficient Approach for Cross-Silo Federated Learning to Rank
Traditional learning-to-rank (LTR) models are usually trained in a centralized approach based upon a large amount of data. However, with the increasing awareness of data privacy, it is harder to collect data from multiple owners as before, and the resultant data isolation problem makes the performance of learned LTR models severely compromised. Inspired by the recent progress in federated learning, we propose a novel framework named Cross-Silo Federated Learning-to-Rank (CS-F-LTR), where the efficiency issue becomes the major bottleneck. To deal with the challenge, we first devise a privacy-preserving cross-party term frequency querying scheme based on sketching algorithms and differential privacy. To further improve the overall efficiency, we propose a new structure named reverse top-K sketch (RTK-Sketch) which significantly accelerates the feature generation process while holding theoretical guarantees on accuracy loss. Extensive experiments conducted on public datasets verify the effectiveness and efficiency of the proposed approach.
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