基于CPN的联邦概念模型形式化建模与验证(英文)-豆柴文库

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基于CPN的联邦概念模型形式化建模与验证(英文)
Introduction
Theincreasingadoptionoffederatedmachinelearninghasledtoademandformoreefficientandeffectivemechanismsformodelingsuchsystems.OnesuchmethodisusingColoredPetriNets(CPNs)toconstructformalmodelsoffederatedconcepts.CPNsprovideaformalandvisualwaytodescribeconcurrentanddistributedsystems,whichisparticularlyusefulinthecontextoffederatedmachinelearningwheremultiplemodelsarebeingtrainedondifferentdatasources.ThegoalofthispaperistodiscusstheuseofCPNsinformalizingandverifyingfederatedconcepts.
Background
Federatedmachinelearninginvolvesthecollaborativetrainingofmachinelearningmodelsondifferentdatasources,whichmaybedistributedacrossdifferentorganizations.Themainchallengeinfederatedlearningistomaintainprivacyandsecuritywhilestillallowingthemodelstolearnfromthedifferentdatasources.Oneapproachtoaddressingthischallengeisthroughtheuseoffederatedlearningframeworkswhichenablesecureandefficientcommunicationandcoordinationbetweenthemodels.
CPNsareamathematicalmodelingtechniquethatcanbeusedtodescribeconcurrentanddistributedsystems.Theyconsistofasetofplaces,transitions,andarcs.Placesrepresentthestateofthesystem,whiletransitionsspecifyhowthesystemtransitionsfromonestatetothenext.Arcsconnecttheplacesandtransitionsanddefinetheflowoftokensbetweenthem.
CPNscanbeusedtomodelcomplexsystemssuchasfederatedmachinelearning.TheformalandvisualrepresentationofCPNsallowsforaclearandconciserepresentationofthesystemunderanalysis.
Methodology
ThefirststepinbuildingaCPNmodelforfederatedmachinelearningistoidentifythekeycomponentsofthesystem.Thecomponentscouldincludethelocalmodels,thefederatedlearningframework,thecommunicationchannelsbetweenthemodels,andthedatasources.Oncethecomponentshavebeenidentified,theycanbemappedontotheplacesandtransitionsoftheCPNmodel.
Thenextstepistodefinetheinitialstateofthemodel,whichincludestheinitialvaluesoftheplaces.Inthecaseoffederatedmachinelearning,theinitialstatecouldbetheinitialvaluesofthemodelparameters,theinitialdatadistribution,andtheinit