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Vertical Federated Learning (FL) is a new paradigm that enables users with non-overlapping attributes of the same data samples to jointly train a model without directly sharing the raw data.
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Smile (Statistical Machine Intelligence and Learning Engine)
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Secureboost: A lossless federated learning framework
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Random Sampling Plus Fake Data: Multidimensional Frequency Estimates With Local Differential Privacy. In International Conference on Information and Knowledge Management (CIKM)
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AHEAD: Adaptive Hierarchical Decomposition for Range Query under Local Differential Privacy. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security (CCS) . 1266–1288
Linkang Du, Zhikun Zhang, Shaojie Bai, Changchang Liu, Shouling Ji, Peng Cheng, and Jiming Chen. 2021 · 2021
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VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning. In Proceedings of the 2021 International Conference on Management of Data (SIGMOD) . 563–576
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Local Differential Privacy for data collection and analysis
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