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1、IEEETRANSACTIONSONKNOWLEDGEANDDATAENGINEERING,VOL.25,NO.11,NOVEMBER20132483DistributedAutonomousOnlineLearning:RegretsandIntrinsicPrivacy-PreservingPropertiesFengYan,ShreyasSundaram,Member,IEEE,S.V.N.Vishwanathan,andYuanQiAbstract—Onlinelearninghasbecomeincreasinglypopularonhandlingmassivedata.Th
2、esequentialnatureofonlinelearning,however,requiresacentralizedlearnertostoredataandupdateparameters.Inthispaper,weconsideronlinelearningwithdistributeddatasources.Theautonomouslearnersupdatelocalparametersbasedonlocaldatasourcesandperiodicallyexchangeinformationwithasmallsubsetofneighborsinacommu
3、nicationnetwork.WederivetheregretboundforstronglyconvexfunctionsthatgeneralizestheworkbyRametal.[1]forconvexfunctions.Moreimportantly,weshowthatouralgorithmhasintrinsicprivacy-preservingproperties,andweprovethesufficientandnecessaryconditionsforprivacypreservationinthenetwork.Theseconditionsimply
4、thatfornetworkswithgreater-than-oneconnectivity,amaliciouslearnercannotreconstructthesubgradients(andsensitiverawdata)ofotherlearners,whichmakesouralgorithmappealinginprivacy-sensitiveapplications.IndexTerms—Onlinelearning,distributedcomputing,privacypreservationÇ1INTRODUCTIONNLINElearninghasemer
5、gedasanattractiveparadigmpassestimationinformationtotheirneighborsinacom-Oinmachinelearninggiventheever-increasingmunicationnetwork.Bydoingso,distributedlearningamountsofdatabeingcollectedeveryday.Itefficientlyavoidssharingoriginal,sensitivedatawithothersandreducesthetrainingtimebyprocessingtheda
6、taonlyonce,storingdatainacentrallocation.assumingthatallthetrainingdataareavailableatacentralInthispaper,weconsiderageneraldistributedautono-location.Formanyapplications,however,thisassumptionmousonlinelearningalgorithmtolearnfromfullydecentra-isproblematic.Forinstance,sensornetworksmaybelizeddat
7、asources.Weaddresstwoimportantquestionsdeployedinrainforestsandcollectdataautonomously.associatedwiththisgeneralalgorithm.ThefirstquestionisThecostoftransmittingallthedatatoacentralservercanhowthedistributedonlinelearn
