Quantifying Bytes:Understanding Practical Value of Data Assets in Federated Learning  

作　　者：Minghao Yao Saiyu Qi Zhen Tian Qian Li Yong Han Haihong Li Yong Qi 

机构地区：[1]School of Computer Science and Technology,Xi’an Jiaotong University,Xi’an 710049,China [2]School of Cyber Science and Engineering,Xi’an Jiaotong University,Xi’an 710049,China

出　　处：《Tsinghua Science and Technology》2025年第1期135-147,共13页清华大学学报自然科学版(英文版)

基　　金：supported by the Natural Science Basic Research Program of Shaanxi Program(No.2024JC-JCQN-67);the Fundamental Research Funds for the Central Universities(Nos.xzy012022083 and xxj032022012);the Shaanxi Province QinChuangYuan“Scientist+Engineer”Team Building Project(No.2022KXJ-054);the National Key Research and Development Program of China(No.2023YFB2703800);the National Natural Science Foundation of China(No.62206217);the China Postdoctoral Science Foundation(Nos.2022M722530 and 2023T160512).

摘　　要：The data asset is emerging as a crucial component in both industrial and commercial applications.Mining valuable knowledge from the data benefits decision-making and business.However,the usage of data assets raises tension between sensitive information protection and value estimation.As an emerging machine learning paradigm,Federated Learning(FL)allows multiple clients to jointly train a global model based on their data without revealing it.This approach harnesses the power of multiple data assets while ensuring their privacy.Despite the benefits,it relies on a central server to manage the training process and lacks quantification of the quality of data assets,which raises privacy and fairness concerns.In this work,we present a novel framework that combines Federated Learning and Blockchain by Shapley value(FLBS)to achieve a good trade-off between privacy and fairness.Specifically,we introduce blockchain in each training round to elect aggregation and evaluation nodes for training,enabling decentralization and contribution-aware incentive distribution,with these nodes functionally separated and able to supervise each other.The experimental results validate the effectiveness of FLBS in estimating contribution even in the presence of heterogeneity and noisy data.

关 键 词：Federated Learning(FL) blockchain FAIRNESS 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]