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Development of a Federated Learning Aggregation Algorithm

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Development of a Federated Learning Aggregation Algorithm

Salarbashishahri, Mohammadreza (2023) Development of a Federated Learning Aggregation Algorithm. Masters thesis, Concordia University.

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Abstract

The Internet of Things is made possible by the recent developments in communication and 5G networks, which enable real-time sensory data transfer between billions of devices. Raw data has no value until it is processed to extract its features. The features can be extracted using a machine learning technique, a common way to use these data. Federated learning (FL) is a platform that enables a group of clients to train a model cooperatively without disclosing their personal information. Traditional federated learning has issues such as data and model poisoning attacks, free-riding attacks, and model divergence caused by clients' non-independent and identically distributed (non-IID) datasets. Because the conventional federated averaging (FedAvg) aggregation algorithm in FL lacks an evaluation technique, it is unable to detect dishonest users or correct the global model's divergence. In this study, we suggest Shapley averaging (ShapAvg), a Shapley-based aggregation technique, to aggregate the global model by analyzing the models of the clients more effectively. Each client's weight in the weighted average under this approach will be proportionate to how much it contributed to the overall model performance. The results demonstrate that while employing non-IID datasets and in the presence of data poisoning or free-riding attacks, our suggested technique overperforms the FedAvg.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Salarbashishahri, Mohammadreza
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:3 April 2023
Thesis Supervisor(s):Cai, Jun
ID Code:992393
Deposited By: Mohammadreza Salarbashishahri
Deposited On:15 Nov 2023 15:26
Last Modified:15 Nov 2023 15:26

References:

[1] Yi Liu, James J. Q. Yu, Jiawen Kang, Dusit Niyato, and Shuyu Zhang. “Privacy-Preserving
Traffic Flow Prediction: A Federated Learning Approach”. In: IEEE Internet of Things Jour-
nal 7.8 (Aug. 2020), pp. 7751–7763. ISSN: 2327-4662. DOI: 10 .1109 / JIOT .2020
.2991401.
[2] Ganta Sruthi, Chokkakula Likitha Ram, Malegam Koushik Sai, Bhanu Pratap Singh, Nikhil
Majhotra, and Neha Sharma. “Cancer Prediction using Machine Learning”. In: 2022 2nd
International Conference on Innovative Practices in Technology and Management (ICIPTM).
Vol. 2. Feb. 2022, pp. 217–221. DOI: 10.1109/ICIPTM54933.2022.9754059.
[3] S. D. Okegbile and Jun Cai. “Edge-assisted human-to-virtual twin connectivity scheme for
human digital twin frameworks”. In: IEEE Vehicular Technology Conference (Spring). Helsinki,
June 2022, pp. 1–6.
[4] S. D. Okegbile, Jun Cai, C. Yi, and D. Niyato. “Human Digital Twin for Personalized Health-
care: Vision, Architecture and Future Directions”. In: IEEE Network. May 2022.
[5] Shuaicheng Ma, Yang Cao, and Li Xiong. “Transparent Contribution Evaluation for Se-
cure Federated Learning on Blockchain”. In: 2021 IEEE 37th International Conference on
Data Engineering Workshops (ICDEW). ISSN: 2473-3490. Apr. 2021, pp. 88–91. DOI: 10
.1109/ICDEW53142.2021.00023.
[6] Ping Li, Jin Li, Zhengan Huang, Tong Li, Chong-Zhi Gao, Siu-Ming Yiu, and Kai Chen.
“Multi-key privacy-preserving deep learning in cloud computing”. en. In: Future Genera-
tion Computer Systems 74 (Sept. 2017), pp. 76–85. ISSN: 0167-739X. DOI: 10 .1016/j
.future.2017.02.006.
[7] Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Franc ̧oise Beaufays,
Sean Augenstein, Hubert Eichner, Chlo ́e Kiddon, and Daniel Ramage. “Federated Learning
for Mobile Keyboard Prediction”. In: arXiv:1811.03604 [cs] (Feb. 2019).
[8] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Ar-
cas. “Communication-Efficient Learning of Deep Networks from Decentralized Data”. en.
In: Artificial Intelligence and Statistics. PMLR, Apr. 2017, pp. 1273–1282.
[9] Reza Shokri and Vitaly Shmatikov. “Privacy-Preserving Deep Learning”. In: Proceedings of
the 22nd ACM SIGSAC Conference on Computer and Communications Security. CCS ’15.
New York, NY, USA: Association for Computing Machinery, Oct. 2015, pp. 1310–1321.
ISBN: 978-1-4503-3832-5. DOI: 10.1145/2810103.2813687.
[10] Pengrui Liu, Xiangrui Xu, and Wei Wang. “Threats, attacks and defenses to federated learn-
ing: issues, taxonomy and perspectives”. In: Cybersecurity 5.1 (Feb. 2022), p. 4. ISSN: 2523-
3246. DOI: 10.1186/s42400-021-00105-6.
[11] Jierui Lin, Min Du, and Jian Liu. Free-riders in Federated Learning: Attacks and Defenses.
arXiv:1911.12560 [cs, stat]. Nov. 2019. DOI: 10.48550/arXiv.1911.12560.
[12] L. S. Shapley. “17. A Value for n-Person Games”. en. In: Contributions to the Theory of
Games (AM-28). Vol. II. Princeton University Press, Mar. 2016, pp. 307–318. ISBN: 978-1-
4008-8197-0. DOI: 10.1515/9781400881970-018.
[13] Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang
Liang, Qiang Yang, Dusit Niyato, and Chunyan Miao. “Federated Learning in Mobile Edge
Networks: A Comprehensive Survey”. In: IEEE Communications Surveys & Tutorials 22.3
(2020). Conference Name: IEEE Communications Surveys & Tutorials, pp. 2031–2063.
ISSN: 1553-877X. DOI: 10.1109/COMST.2020.2986024.
[14] Tran The Anh, Nguyen Cong Luong, Dusit Niyato, Dong In Kim, and Li-Chun Wang. “Ef-
ficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement
Learning Approach”. In: IEEE Wireless Communications Letters 8.5 (Oct. 2019), pp. 1345–
1348. ISSN: 2162-2345. DOI: 10.1109/LWC.2019.2917133.
[15] Dongzhu Liu and Osvaldo Simeone. “Privacy for Free: Wireless Federated Learning via Un-
coded Transmission With Adaptive Power Control”. In: IEEE Journal on Selected Areas in
Communications 39.1 (Jan. 2021), pp. 170–185. ISSN: 1558-0008. DOI: 10 .1109/JSAC
.2020.3036948.
[16] Heinrich von Stackelberg. Market Structure and Equilibrium. en. Berlin Heidelberg: Springer-
Verlag, 2011. ISBN: 978-3-642-12585-0. DOI: 10.1007/978-3-642-12586-7.
[17] Yunus Sarikaya and Ozgur Ercetin. “Motivating Workers in Federated Learning: A Stack-
elberg Game Perspective”. In: IEEE Networking Letters 2.1 (Mar. 2020), pp. 23–27. ISSN:
2576-3156. DOI: 10.1109/LNET.2019.2947144.
[18] Shaohan Feng, Dusit Niyato, Ping Wang, Dong In Kim, and Ying-Chang Liang. “Joint Ser-
vice Pricing and Cooperative Relay Communication for Federated Learning”. In: 2019 In-
ternational Conference on Internet of Things (iThings) and IEEE Green Computing and
Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom)
and IEEE Smart Data (SmartData). July 2019, pp. 815–820. DOI: 10 .1109/iThings/
GreenCom/CPSCom/SmartData.2019.00148.
[19] Yufeng Zhan, Peng Li, Zhihao Qu, Deze Zeng, and Song Guo. “A Learning-Based Incentive
Mechanism for Federated Learning”. In: IEEE Internet of Things Journal 7.7 (July 2020),
pp. 6360–6368. ISSN: 2327-4662. DOI: 10.1109/JIOT.2020.2967772.
[20] Shashi Raj Pandey, Nguyen H. Tran, Mehdi Bennis, Yan Kyaw Tun, Aunas Manzoor, and
Choong Seon Hong. “A Crowdsourcing Framework for On-Device Federated Learning”. In:
IEEE Transactions on Wireless Communications 19.5 (May 2020), pp. 3241–3256. ISSN:
1558-2248. DOI: 10.1109/TWC.2020.2971981.
[21] Jiawen Kang, Zehui Xiong, Dusit Niyato, Shengli Xie, and Junshan Zhang. “Incentive Mech-
anism for Reliable Federated Learning: A Joint Optimization Approach to Combining Repu-
tation and Contract Theory”. In: IEEE Internet of Things Journal 6.6 (Dec. 2019), pp. 10700–
10714. ISSN: 2327-4662. DOI: 10.1109/JIOT.2019.2940820.
[22] Yang Zhao, Jun Zhao, Linshan Jiang, Rui Tan, Dusit Niyato, Zengxiang Li, Lingjuan Lyu, and
Yingbo Liu. “Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices”.
In: IEEE Internet of Things Journal 8.3 (Feb. 2021), pp. 1817–1829. ISSN: 2327-4662. DOI:
10.1109/JIOT.2020.3017377.
[23] Kentaroh Toyoda, Jun Zhao, Allan Neng Sheng Zhang, and P. Takis Mathiopoulos. “Blockchain-
Enabled Federated Learning With Mechanism Design”. In: IEEE Access 8 (2020), pp. 219744–
219756. ISSN: 2169-3536. DOI: 10.1109/ACCESS.2020.3043037.
[24] Sungwook Kim. “Incentive Design and Differential Privacy Based Federated Learning: A
Mechanism Design Perspective”. In: IEEE Access 8 (2020), pp. 187317–187325. ISSN: 2169-
3536. DOI: 10.1109/ACCESS.2020.3030888.
[25] Ningning Ding, Zhixuan Fang, and Jianwei Huang. “Optimal Contract Design for Efficient
Federated Learning With Multi-Dimensional Private Information”. In: IEEE Journal on Se-
lected Areas in Communications 39.1 (Jan. 2021), pp. 186–200. ISSN: 1558-0008. DOI: 10
.1109/JSAC.2020.3036944.
[26] Joohyung Lee, DaeJin Kim, and Dusit Niyato. “Market Analysis of Distributed Learning Re-
source Management for Internet of Things: A Game-Theoretic Approach”. In: IEEE Internet
of Things Journal 7.9 (Sept. 2020), pp. 8430–8439. ISSN: 2327-4662. DOI: 10.1109/JIOT
.2020.2991725.
[27] Zhe Peng, Jianliang Xu, Xiaowen Chu, Shang Gao, Yuan Yao, Rong Gu, and Yuzhe Tang.
“VFChain: Enabling Verifiable and Auditable Federated Learning via Blockchain Systems”.
In: IEEE Transactions on Network Science and Engineering (2021), pp. 1–1. ISSN: 2327-
4697. DOI: 10.1109/TNSE.2021.3050781.
[28] Yang Chen, Xiaoyan Sun, and Yaochu Jin. “Communication-Efficient Federated Deep Learn-
ing With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation”.
In: IEEE Transactions on Neural Networks and Learning Systems 31.10 (Oct. 2020), pp. 4229–
4238. ISSN: 2162-2388. DOI: 10.1109/TNNLS.2019.2953131.
[29] Felix Sattler, Simon Wiedemann, Klaus-Robert M ̈uller, and Wojciech Samek. “Robust and
Communication-Efficient Federated Learning From Non-i.i.d. Data”. In: IEEE Transactions
on Neural Networks and Learning Systems 31.9 (Sept. 2020), pp. 3400–3413. ISSN: 2162-
2388. DOI: 10.1109/TNNLS.2019.2944481.
[30] Siqi Luo, Xu Chen, Qiong Wu, Zhi Zhou, and Shuai Yu. “HFEL: Joint Edge Association
and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning”. In: IEEE
Transactions on Wireless Communications 19.10 (Oct. 2020), pp. 6535–6548. ISSN: 1558-
2248. DOI: 10.1109/TWC.2020.3003744.
[31] Qiong Wu, Kaiwen He, and Xu Chen. “Personalized Federated Learning for Intelligent IoT
Applications: A Cloud-Edge Based Framework”. In: IEEE Open Journal of the Computer
Society 1 (2020), pp. 35–44. ISSN: 2644-1268. DOI: 10.1109/OJCS.2020.2993259.
[32] Lei Feng, Yiqi Zhao, Shaoyong Guo, Xuesong Qiu, Wenjing Li, and Peng Yu. “Blockchain-
based Asynchronous Federated Learning for Internet of Things”. In: IEEE Transactions on
Computers (2021), pp. 1–1. ISSN: 1557-9956. DOI: 10.1109/TC.2021.3072033.
[33] Haoye Chai, Supeng Leng, Yijin Chen, and Ke Zhang. “A Hierarchical Blockchain-Enabled
Federated Learning Algorithm for Knowledge Sharing in Internet of Vehicles”. In: IEEE
Transactions on Intelligent Transportation Systems (2020), pp. 1–12. ISSN: 1558-0016. DOI:
10.1109/TITS.2020.3002712.
[34] Hai Jin, Xiaohai Dai, Jiang Xiao, Baochun Li, Huichuwu Li, and Yan Zhang. “Cross-Cluster
Federated Learning and Blockchain for Internet of Medical Things”. In: IEEE Internet of
Things Journal (2021), pp. 1–1. ISSN: 2327-4662. DOI: 10.1109/JIOT.2021.3081578.
[35] Hong Liu, Shuaipeng Zhang, Pengfei Zhang, Xinqiang Zhou, Xuebin Shao, Geguang Pu, and
Yan Zhang. “Blockchain and Federated Learning for Collaborative Intrusion Detection in
Vehicular Edge Computing”. In: IEEE Transactions on Vehicular Technology (2021), pp. 1–
1. ISSN: 1939-9359. DOI: 10.1109/TVT.2021.3076780.
[36] Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. “Fed-
erated Learning with Non-IID Data”. In: arXiv:1806.00582 [cs, stat] (June 2018).
[37] Te-Chuan Chiu, Yuan-Yao Shih, Ai-Chun Pang, Chieh-Sheng Wang, Wei Weng, and Chun-
Ting Chou. “Semisupervised Distributed Learning With Non-IID Data for AIoT Service Plat-
form”. In: IEEE Internet of Things Journal 7.10 (Oct. 2020), pp. 9266–9277. ISSN: 2327-
4662. DOI: 10.1109/JIOT.2020.2995162.
[38] Wenyu Zhang, Xiumin Wang, Pan Zhou, Weiwei Wu, and Xinglin Zhang. “Client Selection
for Federated Learning With Non-IID Data in Mobile Edge Computing”. In: IEEE Access 9
(2021), pp. 24462–24474. ISSN: 2169-3536. DOI: 10.1109/ACCESS.2021.3056919.
[39] Yunfeng Li, Xiaoling Li, Gangqiang Li, and Zhitao Li. “Privacy Protection in Prosumer En-
ergy Management Based on Federated Learning”. In: IEEE Access 9 (2021), pp. 16707–
16715. ISSN: 2169-3536. DOI: 10.1109/ACCESS.2021.3053573.
[40] Guowen Xu, Hongwei Li, Sen Liu, Kan Yang, and Xiaodong Lin. “VerifyNet: Secure and
Verifiable Federated Learning”. In: IEEE Transactions on Information Forensics and Security
15 (2020), pp. 911–926. ISSN: 1556-6021. DOI: 10.1109/TIFS.2019.2929409.
[41] Giuseppe Ateniese, Luigi V. Mancini, Angelo Spognardi, Antonio Villani, Domenico Vitali,
and Giovanni Felici. “Hacking smart machines with smarter ones: How to extract meaningful
data from machine learning classifiers”. In: International Journal of Security and Networks
10.3 (Sept. 2015), pp. 137–150. ISSN: 1747-8405. DOI: 10.1504/IJSN.2015.071829.
[42] Meng Shen, Huan Wang, Bin Zhang, Liehuang Zhu, Ke Xu, Qi Li, and Xiaojiang Du. “Ex-
ploiting Unintended Property Leakage in Blockchain-Assisted Federated Learning for Intelli-
gent Edge Computing”. In: IEEE Internet of Things Journal 8.4 (Feb. 2021), pp. 2265–2275.
ISSN: 2327-4662. DOI: 10.1109/JIOT.2020.3028110.
[43] Martin Abadi, Andy Chu, Ian Goodfellow, Brendan McMahan, Ilya Mironov, Kunal Tal-
war, and Li Zhang. “Deep Learning with Differential Privacy”. In: 2016, pp. 308–318. URL:
https://arxiv.org/abs/1607.00133 (visited on 05/23/2021).
[44] Nishat I. Mowla, Nguyen H. Tran, Inshil Doh, and Kijoon Chae. “AFRL: Adaptive federated
reinforcement learning for intelligent jamming defense in FANET”. In: Journal of Commu-
nications and Networks 22.3 (June 2020), pp. 244–258. ISSN: 1976-5541. DOI: 10.1109/
JCN.2020.000015.
[45] Wei Yang Bryan Lim, Zehui Xiong, Chunyan Miao, Dusit Niyato, Qiang Yang, Cyril Le-
ung, and H. Vincent Poor. “Hierarchical Incentive Mechanism Design for Federated Ma-
chine Learning in Mobile Networks”. In: IEEE Internet of Things Journal 7.10 (Oct. 2020),
pp. 9575–9588. ISSN: 2327-4662. DOI: 10.1109/JIOT.2020.2985694.
[46] Shiva Raj Pokhrel and Jinho Choi. “Federated Learning With Blockchain for Autonomous
Vehicles: Analysis and Design Challenges”. In: IEEE Transactions on Communications 68.8
(Aug. 2020), pp. 4734–4746. ISSN: 1558-0857. DOI: 10.1109/TCOMM.2020.2990686.
[47] Mohamed Abdur Rahman, M. Shamim Hossain, Mohammad Saiful Islam, Nabil A. Alra-
jeh, and Ghulam Muhammad. “Secure and Provenance Enhanced Internet of Health Things
Framework: A Blockchain Managed Federated Learning Approach”. In: IEEE Access 8
(2020), pp. 205071–205087. ISSN: 2169-3536. DOI: 10.1109/ACCESS.2020.3037474.
[48] Laizhong Cui, Xiaoxin Su, Zhongxing Ming, Ziteng Chen, Shu Yang, Yipeng Zhou, and
Wei Xiao. “CREAT: Blockchain-assisted Compression Algorithm of Federated Learning for
Content Caching in Edge Computing”. In: IEEE Internet of Things Journal (2020), pp. 1–1.
ISSN: 2327-4662. DOI: 10.1109/JIOT.2020.3014370.
[49] Shiva Raj Pokhrel. “Blockchain Brings Trust to Collaborative Drones and LEO Satellites: An
Intelligent Decentralized Learning in the Space”. In: IEEE Sensors Journal (2021), pp. 1–1.
ISSN: 1558-1748. DOI: 10.1109/JSEN.2021.3060185.
[50] Rajesh Kumar, Abdullah Aman Khan, Jay Kumar, A. Zakria, Noorbakhsh Amiri Golilarz,
Simin Zhang, Yang Ting, Chengyu Zheng, and Wenyong Wang. “Blockchain-Federated-
Learning and Deep Learning Models for COVID-19 detection using CT Imaging”. In: IEEE
Sensors Journal (2021), pp. 1–1. ISSN: 1558-1748. DOI: 10.1109/JSEN.2021.3076767.
[51] Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta. “Revisiting Unreason-
able Effectiveness of Data in Deep Learning Era”. In: 2017 IEEE International Conference
on Computer Vision (ICCV). ISSN: 2380-7504. Oct. 2017, pp. 843–852. DOI: 10 .1109/
ICCV.2017.97.
[52] TensorFlow. en. URL: https://www.tensorflow.org/ (visited on 03/11/2023).
[53] TensorFlow Federated. en. URL: https : / / www .tensorflow .org / federated
(visited on 03/11/2023).
[54] MNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges. URL:
http://yann.lecun.com/exdb/mnist/ (visited on 07/10/2022).
[55] Google Colaboratory. en. URL: https://colab.research.google.com/ (visited
on 03/11/2023).
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