University of Oulu

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

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Author: Oh, Seungeun1; Park, Jihong2; Jeong, Eunjeong1;
Organizations: 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 (
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
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.

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Series: IEEE communications letters
ISSN: 1089-7798
ISSN-E: 2373-7891
ISSN-L: 1089-7798
Volume: 24
Issue: 10
Pages: 2211 - 2215
DOI: 10.1109/LCOMM.2020.3003693
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: 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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