Methods and Prospects of Personalized Federated Learning - Details

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Currently,　with　the　advancement　of　artificial　intelligence　research,　artificial　intelligence　is　being　widely　adopted,　and　the　increasing　demand　in　areas　such　as　data　governance　has　led　to　growing　awareness　and　concern　for　privacy　protection,　this　has　promoted　the　popularity　of　the　federated　learning　(FL)　framework.　However,　existing　FL　frameworks　struggle　to　address　heterogeneous　issues　and　personalized　user　needs.　In　response　to　these　challenges,　methods　of　personalized　federated　learning　(PFL)　are　studied　and　prospects　are　proposed.　Firstly,　the　FL　framework　is　outlined　and　its　limitations　are　identified,　leading　to　the　research　motivation　for　PFL　based　on　FL　scenarios.　Subsequently,　the　analysis　of　statistical　heterogeneity,　model　heterogeneity,　communication　heterogeneity,　and　device　heterogeneity　in　PFL　is　conducted,　and　feasible　solutions　are　proposed.　Then,　personalized　algorithms　in　PFL　such　as　client　selection　and　knowledge　distillation　are　categorized,　and　their　innovations　and　shortcomings　are　analyzed.　Finally,　future　research　directions　for　PFL　are　discussed.　©　2024　Journal　of　Computer　Engineering　and　Applications　Beijing　Co.,　Ltd.;　Science　Press.　All　rights　reserved.

Keyword：

Data privacy Federated learning Differential privacy

Author Community：

[ 1 ] [Sun, Yanhua]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Sun, Yanhua]Advanced Information Network Beijing Laboratory, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Wang, Zihang]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 4 ] [Wang, Zihang]Advanced Information Network Beijing Laboratory, Beijing University of Technology, Beijing; 100124, China
[ 5 ] [Liu, Chang]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 6 ] [Liu, Chang]Advanced Information Network Beijing Laboratory, Beijing University of Technology, Beijing; 100124, China
[ 7 ] [Yang, Ruizhe]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 8 ] [Yang, Ruizhe]Advanced Information Network Beijing Laboratory, Beijing University of Technology, Beijing; 100124, China
[ 9 ] [Li, Meng]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 10 ] [Li, Meng]Advanced Information Network Beijing Laboratory, Beijing University of Technology, Beijing; 100124, China
[ 11 ] [Wang, Zhuwei]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 12 ] [Wang, Zhuwei]Advanced Information Network Beijing Laboratory, Beijing University of Technology, Beijing; 100124, China

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Source ：

Computer Engineering and Applications

ISSN： 1002-8331

Year： 2024

Issue： 20

Volume： 60

Page： 68-83

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SCOPUS Cited Count：

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 14

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