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Ericsson Technology Review: Privacy-aware machine learning with low network footprint

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Conventional machine learning (ML) models provide many benefits to mobile network operators, particularly in terms of ensuring consistent QoE. However, on top of creating a substantial network footprint, the large data transfer required by conventional ML models can be problematic from a data privacy perspective. Federated learning (FL) makes it possible to overcome these challenges by bringing “the computation to the data” instead of transferring “the data to the computation.”

The latest Ericsson Technology Review article demonstrates that it is possible to migrate from a conventional ML model to an FL model and significantly reduce the amount of information that is exchanged between different parts of the network, enhancing privacy without negatively impacting accuracy.

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Ericsson Technology Review: Privacy-aware machine learning with low network footprint

  1. 1. ERICSSON TECHNOLOGY PRIVACY-AWARE MACHINELEARNING C H A R T I N G T H E F U T U R E O F I N N O V A T I O N | # 0 9 ∙ 2 0 1 9
  2. 2. ✱ PRIVACY-AWARE MACHINE LEARNING PRIVACY-AWARE MACHINE LEARNING ✱ 2 OCTOBER 21, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ OCTOBER 21, 2019 3 Federated learning makes it possible to train machine learning models without transferring potentially sensitive user data from devices or local deployments to a central server. As such, it addresses privacy concerns at the same time that it improves functionality. Depending on the complexity of the neural network, it can also dramatically reduce the amount of data needed while training a model. KONSTANTINOS VANDIKAS, SELIM ICKIN, GAURAV DIXIT, MICHAEL BUISMAN, JONAS ÅKESON Reliance on artificial intelligence (AI) and automation solutions is growing rapidly in the telecom industry as network complexity continues to expand. The machine learning (ML) models that many mobile network operators (MNOs) use to predict and solve issues before they affect user QoE are just one example. ■Animportantaspectofthe5Gevolutionisthe transformationofengineerednetworksinto continuouslearningnetworksinwhichself- adapting,scalableandintelligentagentscanwork independentlytocontinuouslyimprovequalityand performance.Theseemerging“zero-touch networks”are,andwillcontinuetobe,heavily dependentonMLmodels. Thereal-worldperformanceofanyMLmodel dependsontherelevanceofthedatausedtotrainit. ConventionalMLmodelsdependonthemass transferofdatafromthedevicesordeploymentsites toacentralservertocreatealarge,centralized dataset.Evenincaseswherethecomputationis decentralized,thetrainingofconventionalML modelsstillrequireslarge,centralizeddatasetsand missesoutonusingcomputationalresourcesthat maybeavailableclosertowheredataisgenerated. WhileconventionalMLdeliversahighlevelof accuracy,itcanbeproblematicfromadatasecurity perspective,duetolegalrestrictionsand/orprivacy concerns.Further,thetransferofsomuchdata requiressignificantnetworkresources,whichmeans thatlackofbandwidthanddatatransfercostscanbe anissueinsomesituations.Evenincaseswhereall therequireddataisavailable,relianceona centralizeddatasetformaintenanceandretraining purposescanbecostlyandtimeconsuming. Forbothprivacyandefficiencyreasons,Ericsson believesthatthezero-touchnetworksofthefuture mustbeabletolearnwithoutneedingtotransfer voluminousamountsofdata,performcentralized computationand/orriskexposingsensitive information.Federatedlearning(FL),withitsability todoMLinadecentralizedmanner,isapromising approach. TobetterunderstandthepotentialofFLina telecomenvironment,wehavetesteditinanumber ofusecases,migratingthemodelsfrom conventional,centralizedMLtoFL,usingthe accuracyoftheoriginalmodelasabaseline.Our researchindicatesthattheusageofasimpleneural networkyieldsasignificantreductioninnetwork utilization,duetothesharpdropintheamountof datathatneedstobeshared. Aspartofourwork,wehavealsoidentifiedthe propertiesnecessarytocreateanFLframeworkthat canachievethehighscalabilityandfaulttolerance requiredtosustainseveralFLtasksinparallel. Anotherimportantaspectofourworkinthisarea hasbeenfiguringouthowtotransferanMLmodel thataddressesaspecificandcommonproblem, pretrainedwithinanFLmechanismonexisting networknodestonewlyjoinednetworknodes,so thattheytoocanbenefitfromwhathasbeenlearned previously. Theconceptoffederatedlearning ThecoreconceptbehindFListotrainacentralized modelondecentralizeddatathatneverleavesthe localdatacenterthatgeneratedit.Ratherthan transferring“thedatatothecomputation,”FL transfers“thecomputationtothedata.”[1] Initssimplestform,anFLframeworkmakesuse ofneuralnetworks,trainedlocallyascloseas possibletowherethedataisgenerated/collected. Suchinitialmodelsaredistributedtoseveraldata sourcesandtrainedinparallel.Oncetrained,the weightsofallneurons oftheneuralnetworkare transportedtoacentraldatacenter,wherefederated averagingtakesplaceandanewmodelisproduced andcommunicatedbacktoalltheremoteneural networksthatcontributedtoitscreation. WITH LOW NETWORK FOOTPRINT Privacy-aware machinelearning Terms and abbreviations AI – Artificial Intelligence | AUC – Area Under the Curve | FL – Federated Learning | ML – Machine Learning | MNO – Mobile Network Operator | ROC – Receiver Operating Characteristic
  3. 3. ✱ PRIVACY-AWARE MACHINE LEARNING PRIVACY-AWARE MACHINE LEARNING ✱ 4 OCTOBER 21, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ OCTOBER 21, 2019 5 Figure2illustratesthebasicsystemdesign. Afederationistreatedasataskrun-to-completion, enablingasingleresourcedefinitionofall parametersofthefederationthatislaterdeployedto differentcloud-nativeenvironments.Theresource definitionforthetaskdealsbothwithvariantand invariantpartsofthefederation. Thevariantpartshandlethecharacteristicsofthe FLmodelanditshyperparameters.Theinvariant partshandlethespecificsofcommoncomponents thatcanbereusedbydifferentFLtasks.Invariant partsincludeamessagequeue,themasteroftheFL taskandtheworkerstobedeployedandfederatedin differentdatacenters. Workers(processesrunninginlocaldeployments) aretightlycoupledwiththeunderlyingMLplatform thatisusedtotrainthemodel,whichisimmutable duringthefederation.InourFLexperiments,we selectedTensorFlowtotraintheneuralnetwork, whichisdesignedtobeinterchangeablewithother MLplatformssuchasPyTorch.Communication betweenthemasterandtheworkersisprotected usingTransportLayerSecurityencryptionwith one-timegeneratedpublic/privatekeysthatare discardedassoonasanFLtaskiscompleted. Invariantcomponentscanbereusedbydifferent FLtasks.FLtaskscanrunsequentiallyorinparallel dependingontheavailabilityofresources.Master andworkerprocessesareimplementedasstateless components.Thisdesignchoiceleadstoamore robustframework,sinceitallowsforanFLtaskto failwithoutaffectingotherongoingFLtasks. Faulttolerance Toreducethecomplexityofthecodebaseforboth themasteroftheFLtaskandtheworkersandto keepourimplementationstateless,wechosea messagebustobethesinglepointoffailureinthe designofourFLframework.Thisdesignchoiceis furthermotivatedbytheresearchintocreating highlyscalableandfault-tolerantmessagebusesby combiningleader-electiontechniquesand/orby relyingonpersistentstoragetomaintainthestateof themessagequeueincaseofafailure[4]. Themessageexchangebetweenthemasterofthe FLtaskandtheworkersisimplementedintheform ofassignedtaskssuchas“computenewweights”and “averageweights.”Eachtaskispushedintothe messagequeueandhasadirectrecipient.The recipientmustacknowledgethatithasreceivedthe task.Iftheacknowledgementisnotmade,thetask remainsinthequeue.Incaseofafailure,messages remaininthemessagequeuewhileKubernetes restartsthefailedprocess.Oncetheprocessreaches arunningstateagain,themessagequeue retransmitsanyunacknowledgedtasks. Techniquessuchassecureaggregation[2]and differentialprivacy[3]canbeappliedtofurther ensuretheprivacyandanonymityofthedataorigin. FLcanbeusedtotestandtrainnotonlyon smartphonesandtablets,butonalltypesofdevices. Thismakesitpossibleforself-drivingcarstotrainon aggregatedreal-worlddriverbehavior,forexample, andhospitalstoimprovediagnosticswithout breachingtheprivacyoftheirpatients. Figure1illustratesthebasicarchitectureofanFL lifecycle.Thelightbluedashedlinesindicatethat onlytheaggregatedweightsaresenttotheglobal datalake,asopposedtothelocaldataitself,asisthe caseinconventionalMLmodels.Asaresult,FL makesitpossibletoachievebetterutilizationof resources,minimizedatatransferandpreservethe privacyofthosewhoseinformationisbeing exchanged. ThemainchallengewithanFLapproachisthat thetransitionfromtrainingaconventionalML modelusingacentralizeddatasettoseveralsmaller federatedonesmayintroduceabiasthatimpactsthe accuracyoriginallyachievedbyusingacentralized dataset.Theriskforthisisgreatestinlessreliable andmoreephemeralfederationsthatspanoverto mobiledevices. Itisreasonabletoexpectdatacentersusedby MNOstobesignificantlymorereliablethandevices intermsofdatastorage,computationalresources andgeneralavailability.However,itisimportantto ensurehighfaulttolerance,ascorresponding processesmaystillfailduetolackofresources, softwarebugsorotherissues. Federatedlearningframeworkdesign OurFLframeworkdesignconceptiscloud-native, builtonafederationofKubernetes-baseddata centerslocatedindifferentpartsoftheworld. Weassumerestrictedaccesstoallowforthe executionofcertainprocessesthatarevitaltoFL. Figure 2 Basic design of an FL platform Message bus FL master FL worker 1 FL worker 2 FL worker 3 FL worker N... Figure 1 Overview of federated learning Training (global) Training Inference Data lake (global) Pipelines Data Model distribution Aggregated weights Ericsson Customer Local deployment 1 Training Inference Local deployment 2 Training Inference Local deployment 3 Local storage Local storage Local storage
  4. 4. ✱ PRIVACY-AWARE MACHINE LEARNING PRIVACY-AWARE MACHINE LEARNING ✱ 6 OCTOBER 21, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ OCTOBER 21, 2019 7 Preventivemaintenanceusecase HardwarefaultpredictionisatypicalMLusecase foranMNO.Inthiscase,theaimistopredict whethertherewillbeahardwarefaultataradiounit withinthenextsevendaysbasedondatagenerated intheeight-weekintervalprecedingtheprediction time.TheinputstotheMLmodelconsistofmore than500featuresthatareaggregationsofmultiple performancemanagementcounters,fault managementdatasuchasalarms,weatherdataand thedate/timesincethehardwarehasbeenactivein thefield. Threetrainingscenarios Weperformedtheexperimentsinthreescenarios– centralizedML,isolatedMLandFL. CentralizedMListhebenchmarkscenario.The datasetsfromallfourworkernodesaretransferred toonemasternode,andmodeltrainingisperformed there.Thetrainedmodelisthentransferredand deployedbacktothefourworkernodesforinference. Inthisscenario,allworkernodesuseexactlythe samepretrainedMLmodel. IntheisolatedMLscenario,nodataistransferred fromtheworkernodestoamasternode.Instead, eachworkernodetrainsonitsowndatasetand operatesindependentlyfromtheothers. IntheFLscenario,theworkernodestrainontheir individualdatasetsandsharethelearnedweights fromtheneuralnetworkmodelviathemessage queue.Thesaturationofthemodelaccuraciesis achievedafter15roundsoftheweight-sharingand weight-averagingprocedure.Inthisway,theworker nodescanlearnfromeachotherwithouttransferring theirdatasets. Thepropertiesofeachtrainingscenarioare summarizedinFigure3,TableA. Accuracyresults TableBinFigure3presentstheresultsintheformof medianROCAUC(receiveroperatingcharacteristic areaunderthecurve)scoresobtainedthroughmore than100independentexperiments.Thescores achievedintheFLscenarioaresimilartothose achievedinthecentralizedandisolatedones,while thevarianceoftheFLscoresissignificantlylower comparedwiththeothertwoscenarios. TheresultsinTableBshowthatitisworker1 (south)thatbenefitsfromFL.Theyalsosuggestthat anisolatedMLapproachcanberecommendedin caseswheretheindividualdatasetshaveenough datafortraining.Theonlydrawbackisthatbecause theisolatednodesneverreceiveanyinformation fromothernodes,theywillbemoreconservativein theirresponsetochangesinthedata,withtheriskof potentiallyhigherblindspotsintheindividual datasets. Theimpactofaddingnewworkers Tofacilitatetheaddingofnewworkersatalatertime, informationaboutthecurrentroundmustbe maintainedinthemessageexchangebetweenthe masterandtheworkers.WhenanFLtaskstarts,all workersregistertoroundID0,whichtriggersthe mastertoinitializetherandomweightsand broadcastthesamedistributiontoallworkers.All workerstraininparallelandcontributetothesame traininground.Astheroundsincrease,thefederated model’smaturityincreasesuntilasaturationpointis reached. IfthecurrentroundIDisgreaterthan0,themaster isawarethattheprocessofaveragingofweights hastakenplaceatleastonce,whichmeansthatthe modelisnotatarandominitialstate.Whenanew workerjoinstheFLtask,itsendsitsroundIDas0. Figure 3 Tables relating to the hardware fault prediction use case Centralized Isolated Federated Centralized median (std) Isolated median (std) Federated median (std) Downlink consumption Uplink consumption NoPrivacy preserved Use of overall data Data transfer cost Weight transfer cost Yes Yes 0.91 (0.15)Worker 1 (region 1) 0.89 (0.12) 0.95 (0.05) 0.92 (0.8)Worker 2 (region 2) 0.93 (0.08) 0.93 (0.03) 0.95 (0.16)Worker 3 (region 3) 0.95 (0.13) 0.97 (0.07) 0.97 (0.13)Worker 4 (region 4) 0.97 (0.11) 0.96 (0.05) 0.93 (0.13)Overall 0.93 (0.11) 0.95 (0.05) Federated (MB)Centralized (MB) Table D – Network footprint Table C – Network footprint formulas for each training scenario Table B – ROC AUC scores of workers throughout three scenarios Table A – Summary of scenario definitions FL message size (MB) Rounds Rounds Master 0 0 Worker ID 0 0 Master N * R * Model₀ N * R * Model₀ Worker ID R * Model₀ R * Model₀ i: worker ID N: number of workers R: number of rounds needed until accuracy convergence Model₀: Size of ML model ni : size of dataset in worker ID Worker ID Model₀ ni Master Centralized ML Isolated ML FL ∑ N * Model₀ N i=0 ni Yes No Yes High None None None None Low Workers 19.22,000 0.26 15 4
  5. 5. ✱ PRIVACY-AWARE MACHINE LEARNING PRIVACY-AWARE MACHINE LEARNING ✱ 8 OCTOBER 21, 2019 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ OCTOBER 21, 2019 9 Further reading ❭❭ Privacy Preserving QoE Modeling using Collaborative Learning, October 21, 2019, Selim Ickin, Konstantinos Vandikas, and Markus Fiedler, in the 4th Internet-QoE Workshop: QoE-based Analysis and ManagementofDataCommunicationNetworks(Internet-QoE’19),ACM,NewYork,NY,USA,availableat: https://doi.org/10.1145/3349611.3355548 References 1. Communication-Efficient Learning of Deep Networks from Decentralized Data, February 28, 2017, H. Brendan McMahan et al., available at: https://arxiv.org/pdf/1602.05629.pdf 2. Google, Practical Secure Aggregation for Privacy-Preserving Machine Learning, 2017, K. Bonawitz et al., available at: https://ai.google/research/pubs/pub47246/ 3. Deep Learning with Differential Privacy, October 25, 2016, M. Abadi et al., available at: https://arxiv.org/pdf/1607.00133.pdf 4. Distributed Systems: Principles and Paradigms, 2002, A. Tanenbaum et al., available at: https://www.amazon.com/Distributed-Systems-Principles-Andrew-Tanenbaum/dp/0130888931 ofmessageexchangesisequaltothenumberof regionswherethecompressedversionofthedataset istransferred–thatis,thenumberoflocal deployments. InFL,ontheotherhand,therearemultiple messageexchangesthatconsistofrounds,which referstothenumberoftrainingphasesrequired untiltheaccuracyofthemodelconverges.Message sizeinFLisdeterminedbytheserializedvectorthat containstheneuralweights,whichisdirectlyrelated tothesizeoftheneuralnetwork. TableDinFigure3presentsthenetworkfootprint resultsforthepreventivemaintenanceusecase, showingthattheFLapproachyieldedaoneorderof magnitudereductionindatavolumecomparedwith thecentralizedapproach–adropfrom2,000MBto 19.2MB.Thisdramaticreductioncanbeattributed tothesimplicityoftheneuralnetworkandthe substantiallysmalleramountofdatathatneedstobe transferredintheFLscenario.Inthelongterm,this significantlysmallernetworkfootprintwillenable thecreationofamorecomplexneuralnetworkwith theabilitytodetectmorecomplexpatternsinthe dataset. Conclusion Whileconventionalmachinelearning(ML)models providemanybenefitstomobilenetworkoperators, particularlyintermsofensuringconsistentQoE,the largedatatransferthattheyrequireresultsina substantialnetworkfootprintandcanleadtoprivacy issues.Bybringing“thecomputationtothedata” insteadoftransferring“thedatatothecomputation,” federatedlearning(FL)makesitpossibleto overcomethosechallengesbytrainingacentralized modelondecentralizeddata. Ericsson’sresearchinthisareahasdemonstrated thatitispossibletomigratefromaconventionalML model(trainedusingacompletelycentralized dataset)toafederatedone,significantlyreducing datatransferandprotectingprivacywhileachieving on-paraccuracy.Inlightofourfindings,webelieve thatFLhasanimportantroletoplayintheongoing automationofthetelecomsectorandinthe transitiontothezero-touchnetworksofthefuture. Themaster,whoselatestroundIDisgreaterthan0, recognizestheworkerasnewandimmediately sharesthelateststateofthemodelwiththenew workerafterthefirsthandshake. Figure4illustrateshowaccuracypersistsintheFL modelwhenanewworkerjoins.Inthisexample, threeworkersnumbered0,1and2contributetothe initialtrainingphaseofthemodel,receivethesame randomizedweightmatricesfromthemasternode, andtrainonthesamemodel.Astimepasses, accuracyreachesasaturationpoint.Later,worker3 joinstheFLtask.However,sinceworker3isatits firstround,itisallowedtousethesaturatedmodel, butnotallowedtocontributeintheweight aggregation.Instead,itsweightsarediscarded. Networkfootprintcomparison TableCinFigure3presentsasetofformulasthat canbeusedtodeterminethenetworkfootprintfor centralizedML,isolatedMLandFLscenarios. WhentraininganMLmodelinaconventionalway (asincentralizedML),itisassumedthatthenumber Figure 4 AUC scores before and after a new worker joins the FL task 1.0 0.9 0.8 0.7 0.6 0.5 7000 Initial training Saturation point reached Stability persists 7500 8000 Time +1.55802e9 8500 9000 0 1 2 3 Worker 3 joins the federation and deploys the pretrained model.
  6. 6. ✱ PRIVACY-AWARE MACHINE LEARNING 10 ERICSSON TECHNOLOGY REVIEW ✱ OCTOBER 21, 2019 Konstantinos Vandikas ◆ is a principal researcher at Ericsson Research whose work focuses on the intersection between distributed systems and AI. His background is in distributed systems and service-oriented architectures. He has been with Ericsson Research since 2007, actively evolving research concepts from inception to commercialization. Vandikas has 23 granted patents and over 60 patent applications. He has authored or coauthored more than 20 scientific publications and has participated in technical committees at several conferences in the areas of cloud computing, the Internet of Things and AI. He holds a Ph.D. in computer science from RWTH Aachen University, Germany. Selim Ickin ◆ joined Ericsson Research in 2014 and is currently a senior researcher in the AI department in Sweden. His work has been mostly around building ML prototypes in diverse domains such as to improve network-based video streaming performance, to reduce subscriber churn rate for a video service provider and to reduce network operation cost. He holds a B.Sc. in electrical and electronics engineering from Bilkent University in Ankara, Turkey, as well as an M.Sc. and a Ph.D. in computing from Blekinge Institute of Technology in Sweden. He has authored or coauthored more than 20 publications since 2010. He also has patents in the area of ML within the scope of radio network applications. Gaurav Dixit ◆ joined Ericsson in 2012. He currently heads the Automation and AI Development function for Business Area Managed Services. In earlier roles he was a member of the team that set up the cloud product business within Ericsson. He holds an MBA from the Indian Institute of Management in Lucknow, India, and the Università Bocconi in Milan, Italy, as well as a B.Tech. in electronics and communication engineering from the National Institute of Technology in Jalandhar, India. Michael Buisman ◆ is a strategic systems director at Business Area Managed Services whose work focuses on ML and AI. He joined Ericsson in 2007 and has more than 20 years of experience of delivering new innovations in the telecom industry that drive the transition to a digital world. For the past two years, Buisman and his team have been developing a managed services ML/AI solution that is now being deployed to several customers globally. Buisman holds a BA from the University of Portsmouth in the UK and an MBA from St. Joseph’s University in Philadelphia in the US. Jonas Åkeson ◆ joined Ericsson in 2005. In his current role, he drives the implementation of AI and automation in the three areas that integrate Ericsson’s Managed Services business. He holds an M.Sc. in engineering from Linköping Institute of Technology, Sweden, and a higher education diploma in business economics from Stockholm University, Sweden. theauthors
  7. 7. ISSN 0014-0171 284 23-3333 | Uen © Ericsson AB 2019 Ericsson SE-164 83 Stockholm, Sweden Phone: +46 10 719 0000

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