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VFL-R: a novel framework for multi-party in vertical federated learning

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Abstract

Federated learning (FL) provides a robust distributed framework for machine learning that solves privacy leakage concerns. In the some cases, it is hard to train the FL model with limited communication sources and low computational capabilities for the coordinator. Especially, designing an efficient framework for vertical federated learning (VFL) is a concern, as each party has unique data features. Hence, this paper proposes VFL-R, a novel VFL framework combined with a ring architecture for multi-party cooperative modeling. The VFL-R framework simplifies each party’s intricate communication architecture, defending against semi-honest attacks and reducing the coordinator’s influence in the modeling process. Several experiments challenge our framework’s communication performance against current VFL frameworks, highlighting that for similar test accuracy, VFL-R achieves O(K) number of communications in one communication round and an O(1) communication cost for the coordinator.

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Authors and Affiliations

  1. China University of Petroleum, Qingdao, 266580, China

    Jialin Li, Tongjiang Yan & Pengcheng Ren

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  1. Jialin Li
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  2. Tongjiang Yan
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  3. Pengcheng Ren
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Correspondence to Tongjiang Yan.

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This work was supported by Fundamental Research Funds for the Central Universities (20CX05012A), the Major Scientific and Technological Projects of CNPC under Grant (ZD2019-183-008), and Shandong Provincial Natural Science Foundation of China (ZR2019MF070).

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Li, J., Yan, T. & Ren, P. VFL-R: a novel framework for multi-party in vertical federated learning. Appl Intell 53, 12399–12415 (2023). https://doi.org/10.1007/s10489-022-04111-0

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