S. Oh, J. Park, E. Jeong, H. Kim, M. Bennis and S. -L. Kim, "Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup," in IEEE Communications Letters, vol. 24, no. 10, pp. 2211-2215, Oct. 2020, doi: 10.1109/LCOMM.2020.3003693
Mix2FLD : downlink federated learning after uplink federated distillation with two-way mixup
|Author:||Oh, Seungeun1; Park, Jihong2; Jeong, Eunjeong1;|
1School of Electrical and Electronic Engineering, Yonsei University, 120-749 Seoul, Korea
2School of Information Technology, Deakin University, Geelong, VIC 3220, Australia
3Samsung Research, Samsung Electronics, Seoul, Korea
4Centre for Wireless Communications, University of Oulu, 90500 Oulu, Finland (
|Online Access:||PDF Full Text (PDF, 1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020120399291
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-12-03
This letter proposes a novel communication-efficient and privacy-preserving distributed machine learning framework, coined Mix2FLD. To address uplink-downlink capacity asymmetry, local model outputs are uploaded to a server in the uplink as in federated distillation (FD), whereas global model parameters are downloaded in the downlink as in federated learning (FL). This requires a model output-to-parameter conversion at the server, after collecting additional data samples from devices. To preserve privacy while not compromising accuracy, linearly mixed-up local samples are uploaded, and inversely mixed up across different devices at the server. Numerical evaluations show that Mix2FLD achieves up to 16.7% higher test accuracy while reducing convergence time by up to 18.8% under asymmetric uplink-downlink channels compared to FL.
IEEE communications letters
|Pages:||2211 - 2215|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2018-0-00170, Virtual Presence in Moving Objects through 5G), Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT(NRF-2017R1A2A2A05069810), the Academy of Finland Project MISSION, SMARTER, and the 2019 EU-CHISTERA Projects LeadingEdge and CONNECT.
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