垂直联邦学习中特征维度即服务的真实高效拍卖机制设计

doi:10.11959/j.issn.1000-0801.2023260

Abstract

Abstract:

The vertical federated learning (VFL) architecture was considered, and an auction mechanism using feature dimensions as resources was designed.The mechanism included a trusted third-party central control as the auctioneer and several task demanders as bidders.To prevent fraudulent bidding, the optimal feature-dimension-as-a-service (Optimal-FDaaS) based on the Vickrey-Clarke-Groves (VCG) mechanism was introduced, and the auction winner was determined through integer linear programming.This mechanism maximized social benefits but had high computational complexity.To reduce this, greedy-feature-dimension-as-a-service (Greedy-FDaaS) was proposed.Both mechanisms ensured three properties: truthfulness, individual rationality, and efficiency.Finally, simulation analysis was provided to prove the effectiveness of the proposed mechanisms.

Key words: vertical federated learning, auction game, resource allocation, FDaaS, truthfulness

CLC Number:

TP3-05

Zhongyi LAI, Zhengwei NI, Shaohan FENG. Design of truthful and efficient auction mechanisms for feature-dimension-as-a-service in vertical federated learning[J]. Telecommunications Science, 2023, 39(12): 85-99.

Figures/Tables 9

符号	含义
i, M, I	VFL任务需求者索引、数量、集合
j, N, J	数据特征维度索引、数量、集合
f, K, F	设备索引、数量、集合
v_i, w_i	任务需求者i真实估值与上报竞拍价格估值
D_i, E_i	任务需求者 i 真实需要的与上报的数据特征维度集合
$R$	可用数据特征维度矩阵
$T$	可用设备向量
x_i, $X$	任务需求者i是否被选中标志、相应矩阵
p_i, $P$	任务需求者 i 在拍卖结束后所需要付出的真实支付额、支付向量
b_i, B	任务需求者i的标书、相应集合

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