BFRecSys: A Blockchain-based Federated Matrix Factorization for Recommendation Systems

Dongkun Hou; Jie Zhang

doi:10.1109/BigData59044.2023.10386918

BFRecSys: A Blockchain-based Federated Matrix Factorization for Recommendation Systems

Dongkun Hou^*, Jie Zhang

^*Corresponding author for this work

School of Advanced Technology

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

1 Citation (Scopus)

Abstract

Federated recommendation systems (FRecSys) alleviate the privacy issues of recommendation systems (RecSys) by distributing model training tasks onto users' local devices. However, they still need a single global server to aggregate training results from users and, thereby are vulnerable to server malfunctioning. Besides, they assume all users voluntarily use their data and computing resources to train recommendation model gradients, which is usually impractical. This paper aims to address the aforementioned problems of FRecSys using blockchain. A blockchain-based federated matrix factorization is designed and realized for RecSys, named BFRecSys. It eliminates the need for a single central server by storing the items and user matrices of the matrix factorization in a blockchain. An incentive mechanism is designed and implemented via smart contracts to record participants' contributions. Besides, to further enhance the fairness of the incentive mechanism, a fake gradients detection mechanism based on an unsupervised cluster is designed to evict fake gradients in each iteration. The prototype of BFRecSys is realized, and experiments are carried out on public MovieLens datasets and private Ethereum blockchain. The results show that BFRecSys can significantly improve recommendation performance in terms of training accuracy.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
Editors	Jingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2283-2292
Number of pages	10
ISBN (Electronic)	9798350324457
DOIs	https://doi.org/10.1109/BigData59044.2023.10386918
Publication status	Published - Dec 2023
Event	2023 IEEE International Conference on Big Data, BigData 2023 - Sorrento, Italy Duration: 15 Dec 2023 → 18 Dec 2023

Publication series

Name	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

Conference

Conference	2023 IEEE International Conference on Big Data, BigData 2023
Country/Territory	Italy
City	Sorrento
Period	15/12/23 → 18/12/23

Keywords

Blockchain
Federated Learning
Federated Recommendation System
Matrix Factorization

Access to Document

10.1109/BigData59044.2023.10386918

Cite this

Hou, D., & Zhang, J. (2023). BFRecSys: A Blockchain-based Federated Matrix Factorization for Recommendation Systems. In J. He, T. Palpanas, X. Hu, A. Cuzzocrea, D. Dou, D. Slezak, W. Wang, A. Gruca, J. C.-W. Lin, & R. Agrawal (Eds.), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 (pp. 2283-2292). (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData59044.2023.10386918

Hou, Dongkun ; Zhang, Jie. / BFRecSys : A Blockchain-based Federated Matrix Factorization for Recommendation Systems. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. editor / Jingrui He ; Themis Palpanas ; Xiaohua Hu ; Alfredo Cuzzocrea ; Dejing Dou ; Dominik Slezak ; Wei Wang ; Aleksandra Gruca ; Jerry Chun-Wei Lin ; Rakesh Agrawal. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 2283-2292 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

@inproceedings{56e57fe690564b0faecbdea9005f57dd,

title = "BFRecSys: A Blockchain-based Federated Matrix Factorization for Recommendation Systems",

abstract = "Federated recommendation systems (FRecSys) alleviate the privacy issues of recommendation systems (RecSys) by distributing model training tasks onto users' local devices. However, they still need a single global server to aggregate training results from users and, thereby are vulnerable to server malfunctioning. Besides, they assume all users voluntarily use their data and computing resources to train recommendation model gradients, which is usually impractical. This paper aims to address the aforementioned problems of FRecSys using blockchain. A blockchain-based federated matrix factorization is designed and realized for RecSys, named BFRecSys. It eliminates the need for a single central server by storing the items and user matrices of the matrix factorization in a blockchain. An incentive mechanism is designed and implemented via smart contracts to record participants' contributions. Besides, to further enhance the fairness of the incentive mechanism, a fake gradients detection mechanism based on an unsupervised cluster is designed to evict fake gradients in each iteration. The prototype of BFRecSys is realized, and experiments are carried out on public MovieLens datasets and private Ethereum blockchain. The results show that BFRecSys can significantly improve recommendation performance in terms of training accuracy.",

keywords = "Blockchain, Federated Learning, Federated Recommendation System, Matrix Factorization",

author = "Dongkun Hou and Jie Zhang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data, BigData 2023 ; Conference date: 15-12-2023 Through 18-12-2023",

year = "2023",

month = dec,

doi = "10.1109/BigData59044.2023.10386918",

language = "English",

series = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2283--2292",

editor = "Jingrui He and Themis Palpanas and Xiaohua Hu and Alfredo Cuzzocrea and Dejing Dou and Dominik Slezak and Wei Wang and Aleksandra Gruca and Lin, {Jerry Chun-Wei} and Rakesh Agrawal",

booktitle = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",

}

Hou, D & Zhang, J 2023, BFRecSys: A Blockchain-based Federated Matrix Factorization for Recommendation Systems. in J He, T Palpanas, X Hu, A Cuzzocrea, D Dou, D Slezak, W Wang, A Gruca, JC-W Lin & R Agrawal (eds), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023, Institute of Electrical and Electronics Engineers Inc., pp. 2283-2292, 2023 IEEE International Conference on Big Data, BigData 2023, Sorrento, Italy, 15/12/23. https://doi.org/10.1109/BigData59044.2023.10386918

BFRecSys: A Blockchain-based Federated Matrix Factorization for Recommendation Systems. / Hou, Dongkun; Zhang, Jie.
Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. ed. / Jingrui He; Themis Palpanas; Xiaohua Hu; Alfredo Cuzzocrea; Dejing Dou; Dominik Slezak; Wei Wang; Aleksandra Gruca; Jerry Chun-Wei Lin; Rakesh Agrawal. Institute of Electrical and Electronics Engineers Inc., 2023. p. 2283-2292 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - BFRecSys

T2 - 2023 IEEE International Conference on Big Data, BigData 2023

AU - Hou, Dongkun

AU - Zhang, Jie

PY - 2023/12

Y1 - 2023/12

N2 - Federated recommendation systems (FRecSys) alleviate the privacy issues of recommendation systems (RecSys) by distributing model training tasks onto users' local devices. However, they still need a single global server to aggregate training results from users and, thereby are vulnerable to server malfunctioning. Besides, they assume all users voluntarily use their data and computing resources to train recommendation model gradients, which is usually impractical. This paper aims to address the aforementioned problems of FRecSys using blockchain. A blockchain-based federated matrix factorization is designed and realized for RecSys, named BFRecSys. It eliminates the need for a single central server by storing the items and user matrices of the matrix factorization in a blockchain. An incentive mechanism is designed and implemented via smart contracts to record participants' contributions. Besides, to further enhance the fairness of the incentive mechanism, a fake gradients detection mechanism based on an unsupervised cluster is designed to evict fake gradients in each iteration. The prototype of BFRecSys is realized, and experiments are carried out on public MovieLens datasets and private Ethereum blockchain. The results show that BFRecSys can significantly improve recommendation performance in terms of training accuracy.

AB - Federated recommendation systems (FRecSys) alleviate the privacy issues of recommendation systems (RecSys) by distributing model training tasks onto users' local devices. However, they still need a single global server to aggregate training results from users and, thereby are vulnerable to server malfunctioning. Besides, they assume all users voluntarily use their data and computing resources to train recommendation model gradients, which is usually impractical. This paper aims to address the aforementioned problems of FRecSys using blockchain. A blockchain-based federated matrix factorization is designed and realized for RecSys, named BFRecSys. It eliminates the need for a single central server by storing the items and user matrices of the matrix factorization in a blockchain. An incentive mechanism is designed and implemented via smart contracts to record participants' contributions. Besides, to further enhance the fairness of the incentive mechanism, a fake gradients detection mechanism based on an unsupervised cluster is designed to evict fake gradients in each iteration. The prototype of BFRecSys is realized, and experiments are carried out on public MovieLens datasets and private Ethereum blockchain. The results show that BFRecSys can significantly improve recommendation performance in terms of training accuracy.

KW - Blockchain

KW - Federated Learning

KW - Federated Recommendation System

KW - Matrix Factorization

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U2 - 10.1109/BigData59044.2023.10386918

DO - 10.1109/BigData59044.2023.10386918

M3 - Conference Proceeding

AN - SCOPUS:85184981842

T3 - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

SP - 2283

EP - 2292

BT - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

A2 - He, Jingrui

A2 - Palpanas, Themis

A2 - Hu, Xiaohua

A2 - Cuzzocrea, Alfredo

A2 - Dou, Dejing

A2 - Slezak, Dominik

A2 - Wang, Wei

A2 - Gruca, Aleksandra

A2 - Lin, Jerry Chun-Wei

A2 - Agrawal, Rakesh

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 15 December 2023 through 18 December 2023

ER -

Hou D, Zhang J. BFRecSys: A Blockchain-based Federated Matrix Factorization for Recommendation Systems. In He J, Palpanas T, Hu X, Cuzzocrea A, Dou D, Slezak D, Wang W, Gruca A, Lin JCW, Agrawal R, editors, Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 2283-2292. (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). doi: 10.1109/BigData59044.2023.10386918

BFRecSys: A Blockchain-based Federated Matrix Factorization for Recommendation Systems

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