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Blockchain-based optimized edge node selection and privacy preserved framework for federated learning

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Abstract

Federated learning is a distributed paradigm that trained large-scale neural network models with the participation of multiple edge nodes and data remains on their devices, only sharing the local model updates. With this feature, federated learning is considered a secure solution for data privacy issues. However, the typical FL structure relies on the client–server architecture, which leads to the single-point-of-failure (SPoF) attack, and the random selection of edge devices for model training compromised the accuracy of the model. Furthermore, adversaries try to initiate inference attack i.e., attack on privacy leads to gradient leakage attack. Hence, we proposed a blockchain-based optimized edge node selection and privacy-preserved framework to address the aforementioned issues. We have designed three kinds of smart contracts (1) registration of edge nodes (2) forward bidding to select optimized edge devices for FL model training, and (3) payment settlement and reward smart contracts. Moreover, fully homomorphic encryption with the Cheon, Kim, Kim, and Song (CKKS) method is implemented before transmitting the local model updates to the server. Finally, we evaluated our proposed method on the benchmark dataset and compared it with other state-of-the-art studies. Consequently, we have achieved a higher accuracy and privacy-preserved FL framework with a decentralized nature.

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References

  1. McMahan, Brendan, Moore, Eider, Ramage, Daniel, Hampson, Seth, y Arcas, Blaise Aguera.: Communication-efficient learning of deep networks from decentralized data. volume 54, pages 1273–1282. PMLR, 7 (2017). URL: https://proceedings.mlr.press/v54/mcmahan17a.html

  2. Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: Challenges, methods, and future directions. IEEE Signal Process. Mag. 37, 50–60 (2020). https://doi.org/10.1109/MSP.2020.2975749

    Article  Google Scholar 

  3. Lyu, L., Han, Y., Zhao, J., Yang, Q.: Threats to federated. Learning (2020). https://doi.org/10.1007/978-3-030-63076-8_1

    Article  Google Scholar 

  4. Attia, Q., Jianguo, D., Huansheng, N.: Federated learning attack surface: Taxonomy, cyber defences, challenges, and future directions. Artif. Intell. Rev. 55, 3569–3606 (2022). https://doi.org/10.1007/s10462-021-10098-w

    Article  Google Scholar 

  5. W., Zhibo, S., Mengkai, Z., Zhifei, S., Yang, W., Qian, Q., Hairong Beyond inferring class representatives: User-level privacy leakage from federated learning. 2512–2520. IEEE, 4 (2019). https://doi.org/10.1109/INFOCOM.2019.8737416

  6. X., Chenhao, Q., Youyang, X., Yong, G., Longxiang. Asynchronous federated learning on heterogeneous devices: A survey. arXiv preprint arXiv:2109.04269, (2021)

  7. Kang, Jiawen, Xiong, Zehui, Niyato, Dusit, Yu, Han, Liang, Ying-Chang, Kim, Dong In: Incentive design for efficient federated learning in mobile networks: A contract theory approach. In 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS), pages 1–5. IEEE, (2019). https://doi.org/10.1109/VTS-APWCS.2019.8851649.

  8. Zeng, Rongfei, Zhang, Shixun, Wang, Jiaqi, Chu, Xiaowen: Fmore: An incentive scheme of multi-dimensional auction for federated learning in mec. pages 278–288. IEEE, 11 (2020). https://doi.org/10.1109/ICDCS47774.2020.00094

  9. Nishio, Takayuki, Yonetani, Ryo: Client selection for federated learning with heterogeneous resources in mobile edge. pages 1–7. IEEE, 5 (2019). https://doi.org/10.1109/ICC.2019.8761315

  10. Bouacida, N., Mohapatra, P.: Vulnerabilities in federated learning. IEEE Access 9, 63229–63249 (2021). https://doi.org/10.1109/ACCESS.2021.3075203

    Article  Google Scholar 

  11. Lo, S.K., Liu, Y., Lu, Q., Wang, C., Xu, X., Paik, H.-Y., Zhu, L.: Toward trustworthy ai: Blockchain-based architecture design for accountability and fairness of federated learning systems. IEEE Internet Things J. 10, 3276–3284 (2023). https://doi.org/10.1109/JIOT.2022.3144450

    Article  Google Scholar 

  12. Li, Y., Chen, C., Liu, N., Huang, H., Zheng, Z., Yan, Q.: A blockchain-based decentralized federated learning framework with committee consensus. IEEE Netw. 35, 234–241 (2021). https://doi.org/10.1109/MNET.011.2000263

    Article  Google Scholar 

  13. Qammar, A., Karim, A., Ning, H., Ding, J.: Securing federated learning with blockchain: a systematic literature review. Artif. Intell. Rev. 56, 3951–3985 (2023). https://doi.org/10.1007/s10462-022-10271-9

    Article  Google Scholar 

  14. Zhu, J., Cao, J., Saxena, D., Jiang, S., Ferradi, H.: Blockchain-empowered federated learning: Challenges, solutions, and future directions. ACM Comput. Surv. 55, 1–31 (2023). https://doi.org/10.1145/3570953

    Article  Google Scholar 

  15. Singh, S., Rathore, S., Alfarraj, O., Tolba, A., Yoon, B.: A framework for privacy-preservation of iot healthcare data using federated learning and blockchain technology. Fut. Gener. Comput. Syst. 129, 380–388 (2022). https://doi.org/10.1016/j.future.2021.11.028

    Article  Google Scholar 

  16. Alloghani, M., Alani, M.M., Al-Jumeily, D., Baker, T., Mustafina, J., Hussain, A., Aljaaf, A.J.: A systematic review on the status and progress of homomorphic encryption technologies. J. Inform. Secur. Appl. 48, 102362 (2019). https://doi.org/10.1016/j.jisa.2019.102362

    Article  Google Scholar 

  17. Catak, F.O., Aydin, I., Elezaj, O., Yildirim-Yayilgan, S.: Practical implementation of privacy preserving clustering methods using a partially homomorphic encryption algorithm. Electronics 9, 229 (2020). https://doi.org/10.3390/electronics9020229

    Article  Google Scholar 

  18. Zhang, L., Xu, J., Vijayakumar, P., Sharma, P.K., Ghosh, U. Homomorphic encryption-based privacy-preserving federated learning in iot-enabled healthcare system. IEEE Transactions on Network Science and Engineering, pages 1–17, (2022). https://doi.org/10.1109/TNSE.2022.3185327

  19. Jaspreet, K.G.: ElGamal: Public-Key Cryptosystem. Indiana State University, Math and Computer Science Department, Terre Haute (2015)

    Google Scholar 

  20. nVotes. Multiplicative vs additive homomorphic elgamal, 1 2020. URL: https://nvotes.com/multiplicative-vs-additive-homomorphic-elgamal/

  21. Batool, Z., Zhang, K., Toews, M. Fl-mab: client selection and monetization for blockchain-based federated learning. pages 299–307. ACM, 4 (2022). https://doi.org/10.1145/3477314.3507050

  22. Fu, A., Zhang, X., Xiong, N., Gao, Y., Wang, H., Zhang, J.: Vfl: A verifiable federated learning with privacy-preserving for big data in industrial iot. IEEE Trans. Indus. Inform. 18, 3316–3326 (2022). https://doi.org/10.1109/TII.2020.3036166

    Article  Google Scholar 

  23. Su, Z., Wang, Y., Luan, T.H., Zhang, N., Li, F., Chen, T., Cao, H.: Secure and efficient federated learning for smart grid with edge-cloud collaboration. IEEE Trans. Indus. Inform. 18, 1333–1344 (2022). https://doi.org/10.1109/TII.2021.3095506

    Article  Google Scholar 

  24. Song, M., Wang, Z., Zhang, Z., Song, Y., Wang, Q., Ren, J., Qi, H.: Analyzing user-level privacy attack against federated learning. IEEE J. Select. Areas Commun. 38, 2430–2444 (2020). https://doi.org/10.1109/JSAC.2020.3000372

    Article  Google Scholar 

  25. Zhu, Ligeng, Han, Song: Deep Leakage from Gradients. (2020). https://doi.org/10.1007/978-3-030-63076-8_2

  26. Luo, X., Wu, Y., Xiao, X., Ooi, B.C. Feature inference attack on model predictions in vertical federated learning. pages 181–192. IEEE, 4 (2021). https://doi.org/10.1109/ICDE51399.2021.00023

  27. Feng, L., Zhao, Y., Guo, S., Qiu, X., Li, W., Yu, P.: Bafl: A blockchain-based asynchronous federated learning framework. IEEE Trans. Comput. 71, 1092–1103 (2022). https://doi.org/10.1109/TC.2021.3072033

    Article  Google Scholar 

  28. Qu, Y., Uddin, M.P., Gan, C., Xiang, Y., Gao, L., Yearwood, J.: Blockchain-enabled federated learning: A survey. ACM Comput. Surv. 55(4), 1–35 (2022). https://doi.org/10.1145/3524104

    Article  Google Scholar 

  29. Kim, H., Park, J., Bennis, M., Kim, S.-L.: Blockchained on-device federated learning. IEEE Commun. Lett. 24, 1279–1283 (2020). https://doi.org/10.1109/LCOMM.2019.2921755

    Article  Google Scholar 

  30. Kang, J., Xiong, Z., Niyato, D., Xie, S., Zhang, J.: Incentive mechanism for reliable federated learning: A joint optimization approach to combining reputation and contract theory. IEEE Internet Things J. 6, 10700–10714 (2019). https://doi.org/10.1109/JIOT.2019.2940820

    Article  Google Scholar 

  31. Kang, J., Xiong, Z., Li, X., Zhang, Y., Niyato, D., Leung, C., Miao, C.: Optimizing task assignment for reliable blockchain-empowered federated edge learning. IEEE Trans. Veh. Technol. 70, 1910–1923 (2021). https://doi.org/10.1109/TVT.2021.3055767

    Article  Google Scholar 

  32. Qu, Y., Pokhrel, S.R., Garg, S., Gao, L., Xiang, Y.: A blockchained federated learning framework for cognitive computing in industry 4.0 networks. IEEE Trans. Ind. Inform. 17, 2964–2973 (2021). https://doi.org/10.1109/TII.2020.3007817

    Article  Google Scholar 

  33. Jia, B., Zhang, X., Liu, J., Zhang, Y., Huang, K., Liang, Y.: Blockchain-enabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in iiot. IEEE Trans. Ind. Inform. 18, 4049–4058 (2022). https://doi.org/10.1109/TII.2021.3085960

    Article  Google Scholar 

  34. Habib, M., Rehman, M., Ahmed, D., Salah, K., Damiani, E., Svetinovic, D.: Trustfed: A framework for fair and trustworthy cross-device federated learning in iiot. IEEE Trans. Ind. Inform. 17, 8485–8494 (2021). https://doi.org/10.1109/TII.2021.3075706

    Article  Google Scholar 

  35. Ethereum Foundation. web3.py, 2023. URL: https://web3py.readthedocs.io/en/stable/

  36. Protocol Labs. Ipfs powers the distributed web, 2023. URL: https://ipfs.tech/

  37. Jung, H.C., Andrey, K., Miran, K., Yongsoo, S.: Homomorphic encryption for arithmetic of approximate numbers. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70694-8_15

    Book  Google Scholar 

  38. Ibarrondo, A., Viand, A. Pyfhel: Python for homomorphic encryption libraries. In Proceedings of the 9th on Workshop on Encrypted Computing & Applied Homomorphic Cryptography, pages 11–16, (2021)

  39. Lyubashevsky, Vadim, Peikert, Chris, Regev, Oded: On ideal lattices and learning with errors over rings. J. ACM (JACM) 60(6), 1–35 (2013)

    Article  MathSciNet  Google Scholar 

  40. Albrecht, Martin, Chase, Melissa, Chen, Hao, Ding, Jintai, Goldwasser, Shafi, Gorbunov, Sergey, Halevi, Shai, Hoffstein, Jeffrey, Laine, Kim, Lauter, Kristin: et al. Homomorphic encryption standard. Protecting privacy through homomorphic encryption, pages 31–62, (2021)

  41. Lee, J., Ko, H., Seo, S., Pack, S.: Data distribution-aware online client selection algorithm for federated learning in heterogeneous networks. IEEE Trans. Veh. Technol. 72(1), 1127–1136 (2022). https://doi.org/10.1109/TVT.2022.3205307

    Article  Google Scholar 

  42. Qi, J., Lin, F., Chen, Z., Tang, C., Jia, R., Li, M.: High-quality model aggregation for blockchain-based federated learning via reputation-motivated task participation. IEEE Internet Things J. 9, 18378–18391 (2022). https://doi.org/10.1109/JIOT.2022.3160425

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Haidian District, Beijing, 100083, China

    Attia Qammar, Abdenacer Naouri & Huansheng Ning

  2. Department of Computer Science, Blekinge Institute of Technology, 371 79, Karlskrona, Sweden

    Jianguo Ding

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Qammar, A., Naouri, A., Ding, J. et al. Blockchain-based optimized edge node selection and privacy preserved framework for federated learning. Cluster Comput 27, 3203–3218 (2024). https://doi.org/10.1007/s10586-023-04145-0

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