计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 66-73.doi: 10.11896/jsjkx.220600034
郭艳卿1, 李宇航1, 王湾湾2, 付海燕1, 吴铭侃1, 李祎1
GUO Yan-qing1, LI Yu-hang1, WANG Wan-wan2, FU Hai-yan1, WU Ming-kan1, LI Yi1
摘要： 在水文学、气象学以及保险理赔评估等领域中,通常假设因变量服从Gamma分布,相比多元线性回归,在Gamma分布假设下建立起的Gamma回归具有更出色的拟合效果。以往获得Gamma回归模型的方法是将数据集中起来进行训练,当数据是由多方提供时,在不交换数据的情况下训练满足隐私保护的Gamma回归模型成为需要解决的问题。 为此,提出了一种多方安全的纵向联邦Gamma回归算法,该算法首先使用迭代法推导出纵向联邦Gamma回归模型的对数似然估计表达式,然后结合工程实际确定模型的连接函数,进而构造损失函数建立参数的梯度更新策略,最后对同态加密后的各方参数进行融合更新,获得联邦学习后的Gamma回归模型。在两种公开数据集上进行性能测试,实验结果表明,所提联邦Gamma回归算法在不交换数据的前提下,可有效利用多方数据的价值生成Gamma回归模型,该模型对数据的拟合效果逼近数据在集中情况下学习到的Gamma回归模型,优于单方独立学习获得的Gamma回归模型。
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| [1]NELDE R,JOHN A,ROBERT W W.Generalized linear models[J].Journal of the Royal Statistical Society:Series A(General),1972,135(3):370-384. [2]ENGLAND P D,RICHARD J V.Stochastic claims reserving in general insurance[J].British Actuarial Journal,2002,8(3):443-518. [3]AMIN M,QASIM M,AMANUM,et al.Performance of someridge estimators for the gamma regression model[J].Statistical Papers,2020,61(3):997-1026. [4]WU Z J.The Establishment of SPI_GD Drought index and the research on its test and application[D].Lanzhou:Lanzhou University,2017. [5]MA X M,LUO Z Q.Human activity intensity time and space change research on haba snow mountain nature reserve[J].Journal of AnhuiAgricultural Sciences,2015,43(19):205-208. [6]PAYNTER S,NACHABE M.Regional scale spatio-temporalconsistency of precipitation variables related to water resource management and planning[J].Meteorological Applications,2010,16(3):413-423. [7]GONG M D.The empirical research on agricultural insuranceclassification ratemaking[D].Hunan:Hunan University,2011. [8]ZHONG Z,MENG S W.Comparison and application of gamma regression and lognormla regression[J].Journal of Applied Statistics and Management,2010,29(3):430-436. [9]LI T,SANJABI M,BEIRAMI A,et al.Fair resource allocation in federated learning[J].arXiv:1905.10497,2022. [10]MCMAHAN B,MOORE E,RAMAGE D,et al.Communication-efficient learning of deep networks from decentralized data[C]//Artificial Intelligence and Statistics.PMLR,2017:1273-1282. [11]MCMAHAN H B,MOORE E,RAMAGE D,et al.Federated learning of deep networks using model averaging[J].arXiv:1602.05629,2016. [12]GAO D,JU C,WEI X,et al.HHHFL:hierarchical heteroge-neous horizontal federated learning for electroence phalography[J].arXiv:1909.05784,2019. [13]LIU Y,KANG Y,ZHANG X,et al.A communication efficient vertical federated learning framework[J].arXiv:1912.11187,2019. [14]SHREYA S,XING C,YANG L,et al.Secure and efficient fede-rated transfer learning[C].In 2019 IEEE International Confe-rence on Big Data(Big Data).2019:2569-2576. [15]HARD A,RAO K,MATHEWS R,et al.Federated learning for mobile keyboard prediction[J].arXiv:1811.03604,2018. [16]LI T,ANIT K S,AMEET T,et al.Federated learning:Challenges,methods,and future directions[J].IEEE Signal Processing Magazine,2020,37(3):50-60. [17]KAIROUZ P,MCMAHAN H B,AVENT B.Advances and open problems in federated learning[J].Foundations and Trends© in Machine Learning,2021,14(1/2):1-210. [18]MOTHUKURI V,PARIZI R M,POURIYEH S,et al.A survey on security and privacy of federated learning[J].Future Generation Computer Systems,2021,115:619-640. [19]WANG J Z,KONG L W,HUANG Z C,et al.Summary of fede-rated learning algorithms[J].Big Data,2020,6(6):64-82. [20]YANG K,FAN T,CHEN T J,et al.A quasinewton methodbased vertical federated learning framework for logistic regression[J].arXiv:1912.00513,2019. [21]YANG S,REN B,ZHOU X,et al.Parallel distributed logistic regression for vertical federated learning without thirdparty coordinator[J].arXiv:1911.09824,2019. [22]LIU Y,LIU Y T,LIU Z,et al.Federated Forest[J].arXiv:1905.10053,2020. [23]CHENG K,FAN T,JIN Y,et al.Secureboost:A lossless federated learning framework[J].IEEE Intelligent Systems,2021,36(6):87-98. [24]YANG Q,LIU Y,CHEN T,et al.Federated machine learning:concept and applications[J].ACM Transactions on Intelligent Systems and Technology,2019,10(2):1-19. [25]LI Q,WEN Z Y,WU Z M,et al.A survey on federated learning systems:vision,hype and reality for data privacy and protection[J].arXiv:1907.09693,2022. [26]JAKUB K H,BRENDAN M,DANIEL R,et al.Federated optimization:distributed machine learning for on-device intelligence[J].arXiv:1610.02527,2022. [27]WANG J Y,LIU Q H,LIANG H,et al.Tackling the objective inconsistency problem in heterogeneous federated optimization[C]//Advances in Neural Information Processing Systems.2020:7611-7623. [28]LUO X,WU Y,XIAO X,et al.Feature inference attack on mo-del predictions in vertical federated learning[C]//International Conference on Data Engineering.2021:181-192. [29]WANC P,CHEN Q.Robust federated learning with attack-adaptive aggregation[J].arXiv:2102.05257,2021. [30]HAZRAT A,TANVIR A,MOWAFA H,et al.Federated Lear-ning and Internet of Medical Things－Opportunities and Challenges[J].Studies in Health Technology and Informatics,2022,295:201-204. [31]WEN Y,CHEN M.Medical Data Sharing Scheme Combinedwith Federal Learning and Blockchain[J].Computer Enginee-ring,2022,48(5):145-153,161. [32]WANG S S,CHEN J Y,LU Y N.COVID-19 chest CT image segmentation based on federated learning and blockchain[J].Journal of Jilin University,2021,51(6):2164-2173. [33]PINKAS B,SCHNEIDER T,ZOHNER M.Scalable private set intersection based on OT extension[J].ACM Transactions on Privacy and Security(TOPS),2018,21(2):1-35. [34]TANG Y Y.Gamma distribution and gamma regression[D].Yangzhou:Yangzhou University,2017. [35]RIVEST R L,ADLEMAN L M,DERTOUZOS M L.On data banks and privacy homomorphisms[J].Foundations of secure computation,1978,4(11):169-180. |
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