Skip to main content
SpringerLink
Log in

An approach based on NSGA-III algorithm for solving the multi-objective federated learning optimization problem

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

As the sheer volume of data continues to surge across both private and public networks, the potential for leveraging Machine Learning (ML) to streamline complex supply chains becomes increasingly viable. However, conventional centralized ML and data analytics systems are fraught with vulnerabilities, such as susceptibility to data breaches, loss of system control, and other malicious attacks. Enter Federated Learning (FL), a promising paradigm within ML that prioritizes data confidentiality and decentralized model training. Nevertheless, to harness its full potential and ensure optimal performance, an optimization process is required. This optimization challenge is further compounded by the fact that it falls into the category of NP-hard scheduling problems. Moreover, recent novel attacks on FL architecture have raised substantial security concerns. In this paper, we present a novel approach that marries Blockchain technology with the third-generation Non-dominated Sorting Genetic Algorithm (NSGA-III) to fortify the defense against attacks targeting FL algorithms operating within Internet of Things (IoT) systems. Our holistic optimization approach, underpinned by Blockchain, guarantees not only the veracity of trained models but also optimizes processing time and reduces the success rate of potential attacks. Consequently, we propose a hybrid methodology that integrates Blockchain and FL optimization, safeguarding ML models from a spectrum of threats while preserving user privacy. To tackle the bi-objective FL optimization problem, we introduce an efficient NSGA-III algorithm. Our rigorous experiments demonstrate the superior efficacy of our proposed solution compared to the current state-of-the-art approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Algorithm 2
Algorithm 3
Algorithm 4
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Shah N (2004) Pharmaceutical supply chains: key issues and strategies for optimisation. Comput Chem Eng 28(6–7):929–941

    Article  Google Scholar 

  2. Srai JS, Harrington T, Alinaghian L, Phillips M (2015) Evaluating the potential for the continuous processing of pharmaceutical products—a supply network perspective. Chem Eng Process 97:248–258

    Article  Google Scholar 

  3. Trokanas N, Srai JS (2017) Towards an ontological backbone for pharmaceutical digital supply chains. In: Computer aided chemical engineering, vol 40. Elsevier, Amsterdam. pp 2329–2334

  4. Fanning K, Centers DP (2016) Blockchain and its coming impact on financial services. J Corp Account Finance 27(5):53–57

    Article  Google Scholar 

  5. Addair T. Decentralized and distributed machine learning model training with actors

  6. LanZou. Chapter 5—meta-learning for computer vision, meta-learning, 2023, pp 91–208. https://doi.org/10.1016/B978-0-323-89931-4.00012-2

  7. McMahan B, Moore E, Ramage D, Hampson S, y Arcas BA (2017). Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics. PMLR, pp 1273–1282

  8. Hard A, Rao K, Mathews R, Ramaswamy S, Beaufays F, Augenstein S, Ramage D (2018) Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604

  9. Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Roselander J (2019) Towards federated learning at scale: system design. Proc Mach Learn Syst 1:374–388

    Google Scholar 

  10. Caldas S et al (2018) Leaf: a benchmark for federated settings. arXiv preprint arXiv:1812.01097

  11. Liu Y et al (2020) Fedvision: an online visual object detection platform powered by federated learning. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, no 08

  12. Zhuo HH, Feng W, Lin Y, Xu Q, Yang Q (2019) Federated deep reinforcement learning. arXiv preprint arXiv:1901.08277.

  13. Lee J, Sun J, Wang F, Wang S, Jun CH, Jiang X (2018) Privacy-preserving patient similarity learning in a federated environment: development and analysis. JMIR Med Inform 6(2):e7744

    Article  Google Scholar 

  14. Huang L, Yin Y, Fu Z, Zhang S, Deng H, Liu D (2020) LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data. PLoS One 15(4):e0230706

    Article  Google Scholar 

  15. Gao D et al (2019) Hhhfl: hierarchical heterogeneous horizontal federated learning for electroencephalography. arXiv preprint arXiv:1909.05784 (2019)

  16. Huang L et al (2019) Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. J Biomed Inform 99:103291

    Article  Google Scholar 

  17. Li W et al (2019) Privacy-preserving federated brain tumour segmentation. In: Machine learning in medical imaging: 10th international workshop, MLMI 2019, held in conjunction with MICCAI 2019, Shenzhen, China, 13 October, 2019, Proceedings 10. Springer International Publishing

  18. Yang Q et al (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol (TIST). 10(2):1–9

    Article  Google Scholar 

  19. Ming Y (2017) A survey on visualization for explainable classifiers. Hong Kong

  20. Li Q et al (2021) Inspecting the running process of horizontal federated learning via visual analytics. IEEE Trans Vis Comput Graph 28(12):4085–100

    Article  Google Scholar 

  21. Dittmann G, Jelitto J (2019) A blockchain proxy for lightweight iot devices. In: 2019 Crypto valley conference on blockchain technology (CVCBT). IEEE

  22. Le-Dang Q, Le-Ngoc T (2019) Scalable blockchain-based architecture for massive IoT reconfiguration. In: 2019 IEEE Canadian conference of electrical and computer engineering (CCECE). IEEE

  23. Malik S et al (2019) Trustchain: trust management in blockchain and iot supported supply chains. In: 2019 ieee international conference on Blockchain (Blockchain). IEEE

  24. Truong HT, Almeida M et al (2019) Towards secure and decentralized sharing of IoT data. In: 2019 IEEE international conference on Blockchain (Blockchain). IEEE

  25. Islam MA, Madria S (2019) A permissioned blockchain based access control system for IOT. In: 2019 IEEE international conference on Blockchain (Blockchain). IEEE

  26. Özyılmaz KR, Yurdakul A (2017) Integrating low-power IoT devices to a blockchain-based infrastructure: work-in-progress. In: Proceedings of the thirteenth ACM international conference on embedded software 2017 Companion

  27. Unal D, Hammoudeh M, Kiraz MS (2020) Policy specification and verification for blockchain and smart contracts in 5G networks. ICT Express. 6(1):43–7

    Article  Google Scholar 

  28. Ferrag MA et al (2018) Blockchain technologies for the internet of things: Research issues and challenges. IEEE Internet of Things J 6(2):2188–2204

    Article  Google Scholar 

  29. Jiang W et al (2023) IoT Access Control Model Based on Blockchain and Trusted Execution Environment. Processes 11(3):723

    Article  Google Scholar 

  30. Urien P (2018) Blockchain IoT (BIoT): a new direction for solving Internet of Things security and trust issues. In: 2018 3rd Cloudification of the Internet of Things (CIoT). IEEE

  31. Singh M, Singh A, Kim S (2018) Blockchain: a game changer for securing IoT data. In: 2018 IEEE 4th world forum on internet of things (WF-IoT). IEEE, 2018

  32. Wu C et al (2020) Mitigating backdoor attacks in federated learning. arXiv preprint arXiv:2011.01767

  33. Bagdasaryan E, Shmatikov V (2021) Blind backdoors in deep learning models. In: 30th USENIX security symposium (USENIX Security 21)

  34. Zhao Y et al (2022) Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks. Concurr Comput Pract Exp 34(7):e5906

    Article  Google Scholar 

  35. Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248

    Article  Google Scholar 

  36. Abbass HA (2014) Calibrating independent component analysis with Laplacian reference for real-time EEG artifact removal. In: Neural information processing: 21st international conference, ICONIP 2014, Kuching, Malaysia, November 3–6, 2014. Proceedings, Part III 21. Springer International Publishing

  37. Goh SK et al (2014) Artifact removal from EEG using a multiobjective independent component analysis model. In Neural information processing: 21st international conference, ICONIP 2014, Kuching, Malaysia, November 3–6, 2014. Proceedings, Part I 21. Springer International Publishing

  38. Thomas, SA, and Jin Y (2014) Reconstructing biological gene regulatory networks: where optimization meets big data. Evol Intel 7: 29–47

  39. Cevher V, Becker S, Schmidt M (2014) Convex optimization for big data: Scalable, randomized, and parallel algorithms for big data analytics. IEEE Signal Process Mag 31(5):32–43

    Article  Google Scholar 

  40. Slavakis K, Giannakis GB, Mateos G (2014) Modeling and optimization for big data analytics: (statistical) learning tools for our era of data deluge. IEEE Signal Process Mag 31(5):18–31

    Article  Google Scholar 

  41. Daneshmand A, Facchinei F, Kungurtsev V, Scutari G (2015) Hybrid random/deterministic parallel algorithms for convex and nonconvex big data optimization. IEEE Trans Signal Proces 63(15):3914–3929. https://doi.org/10.1109/tsp.2015.2436357

    Article  MathSciNet  Google Scholar 

  42. Facchinei F, Scutari G, Sagratella S (2015) Parallel selective algorithms for nonconvex big data optimization. IEEE Trans Signal Process 63(7):1874–1889

    Article  MathSciNet  Google Scholar 

  43. Richtárik P, Takáč M (2016) Parallel coordinate descent methods for big data optimization. Math Program 156:433–484

    Article  MathSciNet  Google Scholar 

  44. Zhang Y et al (2015) A multi-agent genetic algorithm for big optimization problems. In: 2015 IEEE congress on evolutionary computation (CEC). IEEE, 2015

  45. Elsayed S, Sarker R (2016) Differential evolution framework for big data optimization. Memet Comput 8:17–33

    Article  Google Scholar 

  46. Yi J-H et al (2018) An improved NSGA-III algorithm with adaptive mutation operator for Big Data optimization problems. Future Gener Comput Syst 88:571–585

    Article  Google Scholar 

  47. Azzouz R, Bechikh S, Ben Said L (2017) Dynamic multiobjective optimization using evolutionary algorithms: a survey. In: Recent advances in evolutionary multi-objective optimization, pp 31–70

  48. Seada H, Deb K (2014) U-NSGA-III: a unified evolutionary algorithm for single, multiple, and many-objective optimization. COIN report 2014022

  49. Ibrahim A et al (2016) EliteNSGA-III: an improved evolutionary manyobjective optimization algorithm. In: 2016 IEEE congress on evolutionary computation (CEC). IEEE

  50. Prakash R, Ranvijay (2023) Multi-operator based improved environmental adaptation method for application in real-world optimization problems. Int J Inf Technol . https://doi.org/10.1007/s41870-023-01505-2

  51. Punam P, Chatterjee K (2022) Design and implementation of hybrid consensus mechanism for IoT based healthcare system security. Int J Inf Technol 14(3):1381–1396

    Google Scholar 

  52. Alfalqi K, Bellaiche M (2023) Emergency events detection based on integration of federated learning and active learning. Int J Inf Technol 15(6):2863–2876

    Google Scholar 

  53. Singhal D, Ahuja L, Seth A (2023) POSMETER: proofof-stake blockchain for enhanced smart meter data security. Int J Inf Technol 16:1171–1184

    Google Scholar 

  54. Devidas S, Rukma Rekha N, Subba Rao YV (2023) Identity verifiable ring signature scheme for privacy protection in blockchain. Int J Inf Technol 15:2559–2568

    Google Scholar 

  55. Pabitha P et al (2023) ModChain: a hybridized secure and scaling blockchain framework for IoT environment. Int J Inf Technol 15(3):1741–1754

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Sciences, Faculty of Sciences of Gafsa, Gafsa, Tunisia

    Issam Zidi

  2. Unit of Scientific Research, Applied College, Qassim University, Buraydah, Saudi Arabia

    Ibrahim Issaoui

  3. Department of Information Technology, College of Computer, Qassim University, KSA, Buraydah, Saudi Arabia

    Salim El Khediri & Rehan Ullah Khan

Authors
  1. Issam Zidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ibrahim Issaoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salim El Khediri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rehan Ullah Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Salim El Khediri.

Ethics declarations

Conflict of interest

This manuscript has not been published and is not under consideration for publication elsewhere. We have no conflicts of interest to disclose.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zidi, I., Issaoui, I., El Khediri, S. et al. An approach based on NSGA-III algorithm for solving the multi-objective federated learning optimization problem. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01801-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41870-024-01801-5

Keywords

Search

Navigation