Presentation 2023-03-02
Communication-Efficient Federated Learning with Gradient Boosting Decision Trees
Kotaro Shimamura, Shinya Takamaeda,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Federated learning (FL) is a machine learning method in which clients learn cooperatively without disclosing private data to others. Since the current GBDT-based FL has a large communication volume, we propose a method that uses decision tree subtrees for learning. A serverless method is also proposed to further reduce the communication volume by reducing the learning cost of the leaf weights.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) federated learning / gradient boosting decision trees / communication volume / security
Paper # VLD2022-99,HWS2022-70
Date of Issue 2023-02-22 (VLD, HWS)

Conference Information
Committee HWS / VLD
Conference Date 2023/3/1(4days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Makoto Nagata(Kobe Univ.) / Minako Ikeda(NTT)
Vice Chair Yuichi Hayashi(NAIST) / Daisuke Suzuki(Mitsubishi Electric) / Shigetoshi Nakatake(Univ. of Kitakyushu)
Secretary Yuichi Hayashi(Sony Semiconductor Solutions) / Daisuke Suzuki(NAIST) / Shigetoshi Nakatake(NBS)
Assistant / Takuma Nishimoto(Hitachi)

Paper Information
Registration To Technical Committee on Hardware Security / Technical Committee on VLSI Design Technologies
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Communication-Efficient Federated Learning with Gradient Boosting Decision Trees
Sub Title (in English)
Keyword(1) federated learning
Keyword(2) gradient boosting decision trees
Keyword(3) communication volume
Keyword(4) security
1st Author's Name Kotaro Shimamura
1st Author's Affiliation The University of Tokyo(UTokyo)
2nd Author's Name Shinya Takamaeda
2nd Author's Affiliation The University of Tokyo(UTokyo)
Date 2023-03-02
Paper # VLD2022-99,HWS2022-70
Volume (vol) vol.122
Number (no) VLD-402,HWS-403
Page pp.pp.137-142(VLD), pp.137-142(HWS),
#Pages 6
Date of Issue 2023-02-22 (VLD, HWS)