首页|A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends
A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends
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国家科技期刊平台
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When data privacy is imposed as a necessity,Feder-ated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Fur-thermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describ-ing the main components of FL,from key elements to FL cate-gories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scien-tists willing to explore the capabilities of FL in practical applica-tions.
Data privacydistributed machine learningfeder-ated learningsoftware frameworks
M.Victoria Luzón、Nuria Rodríguez-Barroso、Alberto Argente-Garrido、Daniel Jiménez-López、Jose M.Moyano、Javier Del Ser、Weiping Ding、Francisco Herrera
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Department of Software Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada 18071, Spain
Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada 18071, Spain
Department of Communications Engineering, University of the Basque Country (UPV/EHU), and also with TECNALIA, Basque Research & Technology Alliance (BRTA), Spain
School of Information Science and Technology, Nantong University, Nantong 226019, China
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Research and Development&I,SpainMCIN/AEI/10.13039/501100011033MCIN/AEI/10.13039/501100011033ESF Investing in your future,Spainpostdoctoral Juan de la Cierva FormaciónMCIN/AEI/10.13039/5011000 11033European Union NextGenerationEU/PRTRSpanish Centro para el Desarrollo Tecnológico Industrial(CDTI)through the AI4ES projectDepartment of Education of the Basque Government(consolidated research group MATHMODE)
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