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Communication-Efficient Federated Learning

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Federated Learning for IoT Applications

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

Owing to the universal availability of 5G networking networks, both business and academia have started to explore 6G interchanges. 6G is widely intended to be based on artificial intelligence’s pervasiveness in order to accomplish machine learning dependent on evidence (ML) arrangements in a diverse environment and large-scale organizations (AI). Traditional machine learning procedures, on the other side, include centralized data storage and processing by a single employee. Due to escalating security issues, this increasingly has become restriction on a wide range of applications in daily life. Federated learning, as a growing distributed AI method with a security-conscious design, is particularly appealing for a variety of remote applications and is being viewed as one of the critical solutions for achieving universal AI in 6G. We’ll go into the partnership between 6G and federated learning in this chapter, as well as some future 6G federated learning implementations. Then, in relation to 6G interchanges, we go through main technical problems, compare federated learning approaches, and answer unanswered questions for potential federated learning analysis.

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References

  1. K.B. Letaief, W. Chen, Y. Shi, J. Zhang, Y.-J.A. Zhang, The roadmap to 6g: Ai empowered wireless networks. IEEE Commun. Mag. 57(8), 84–90 (2019)

    Article  Google Scholar 

  2. Y. Xiao, G. Shi, M. Krunz, Towards ubiquitous AI in 6g with federated learning. arXiv preprint arXiv, 2004.13563 (2020)

    Google Scholar 

  3. K. David, H. Berndt, 6g vision and requirements: Is there any need for beyond 5g? IEEE Veh. Technol. Mag. 13(3), 72–80 (2018)

    Article  Google Scholar 

  4. S. Dang, O. Amin, B. Shihada, M.S. Alouini, What should 6g be? Nat. Electron. 3(1), 20–29 (2020)

    Article  Google Scholar 

  5. S. Niknam, H.S. Dhillon, J.H. Reed, Federated learning for wireless communications: Motivation, opportunities and challenges. arXiv preprint arXiv, 1908.06847 (2019)

    Google Scholar 

  6. J. Konecˇny`, H.B. McMahan, F.X. Yu, P. Richta’rik, A.T. Suresh, D. Bacon, Federated learning: Strategies for improving communication efficiency, arXiv preprint arXiv:1610.05492 (2016)

    Google Scholar 

  7. S.P. Yadav, K.K. Agrawal, B.S. Bhati, et al., Blockchain-based cryptocurrency regulation: an overview. Comput. Econ. (2020). https://doi.org/10.1007/s10614-020-10050-0

  8. Y. Shi, K. Yang, T. Jiang, J. Zhang, K.B. Letaief, Communication-efficient edge ai: Algorithms and systems. arXiv preprint arXiv, 2002.09668 (2020)

    Google Scholar 

  9. Y. Liu, J. Peng, J. Kang, A.M. Iliyasu, D. Niyato, A.A.A. El- Latif, A secure federated learning framework for 5g networks. arXiv preprint arXiv, 2005.05752 (2020)

    Google Scholar 

  10. Y. Lin, S. Han, H. Mao, Y. Wang, B. Dally, Deep gradient compression: reducing the communication bandwidth for distributed training, in International Conference on Learning Representations, 2018. [Online]. Available: https://openreview.net/forum?id=SkhQHMW0W

  11. A. Jain, K. Kishor, Financial supervision and management system using ml algorithm. Solid State Technol. 63(6), 18974–18982 (2020)

    Google Scholar 

  12. B. McMahan et al., Communication-efficient learning of deep networks from decentralized data. Proc. Int’l. Conf. Artificial Intell. Stat. (AISTATS) 54, 1273–1282 (2017)

    Google Scholar 

  13. T. Li, A.K. Sahu, A. Talwalkar, V. Smith, Federated learning: challenges, methods, and future directions. IEEE Signal Process. Mag. 37(3), 50–60 (2020)

    Article  Google Scholar 

  14. J. Kang, Z. Xiong, D. Niyato, Y. Zou, Y. Zhang, M. Guizani, Reliable federated learning for mobile networks. IEEE Wireless Communicat. 27(2), 72–80 (2020)

    Article  Google Scholar 

  15. R. Moro-Aguilar, The new commercial suborbital vehicles: An opportunity for scientific and microgravity research. Microgravity Sci. Technol. 26(4), 219–227 (2014)

    Article  Google Scholar 

  16. S.S. Hassan, C.S. Hong, Network utility maximization for 6G maritime communication in deep waters. J. Korean Informat. Sci. Soc., 957–959 (2019)

    Google Scholar 

  17. B. Sliwa, R. Falkenberg, C. Wietfeld, Towards cooperative data rate prediction for future mobile and vehicular 6G networks, in 2020 2nd 6G Wireless Summit (6G SUMMIT), (IEEE, 2020), pp. 1–5

    Google Scholar 

  18. Y. Qian, M. Chen, J. Chen, M.S. Hossain, A. Alamri, Secure enforcement in cognitive internet of vehicles. IEEE Internet Things J. 5(2), 1242–1250 (2018)

    Article  Google Scholar 

  19. J. Scott, A. Stevenson, H. Lupa, Space tourism: An acceleration physiologist’s perspective. Aviat. Space Environ. Med. 83(3) (2012)

    Google Scholar 

  20. N. Henbest, Private space travel: diary of an astronaut in waiting. New Scient. 220(2944), 41–43 (2013)

    Article  Google Scholar 

  21. K. Kishor, P. Nand, P. Agarwal, Subnet based ad hoc network algorithm reducing energy consumption in manet. Int. J. Appl. Eng. Res. 12(22), 11796–11802 (2017)

    Google Scholar 

  22. K. Kishor, P. Nand, P. Agarwal, Notice of retraction design adaptive subnetting hybrid gateway MANET protocol on the basis of dynamic TTL value adjustment. Aptikom J. Comput. Sci. Informat. Technol. 3(2), 59–65 (2018)

    Article  Google Scholar 

  23. K. Kishor, P. Nand, P. Agarwal, Secure and efficient subnet routing protocol for MANET. Execut. Ed. 9(12), 200 (2018)

    Google Scholar 

  24. S.P. Yadav, D.P. Mahato, N.T.D. Linh, Distributed artificial intelligence: a modern approach, 1st edn. (CRC Press, 2020). https://doi.org/10.1201/9781003038467

    Book  Google Scholar 

  25. S.P. Yadav, Emotion recognition model based on facial expressions. Multimed. Tools Appl. (2021). https://doi.org/10.1007/s11042-021-10962-5

  26. Z. Zhang, Y. Xiao, Z. Ma, M. Xiao, Z. Ding, X. Lei, G.K. Karagiannidis, P. Fan, 6g wireless networks: Vision, requirements, architecture, and key technologies. IEEE Veh. Technol. Mag. 14(3), 28–41 (2019)

    Article  Google Scholar 

  27. S. Nayak, R. Patgiri, 6g communication technology: A vision on intelligent healthcare. arXiv preprint arXiv, 2005.07532 (2020)

    Google Scholar 

  28. M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, M. Zorzi, To- ward 6g networks: Use cases and technologies. IEEE Commun. Mag. 58(3), 55–61 (2020)

    Article  Google Scholar 

  29. Y. Liu, J.J.Q. Yu, J. Kang, D. Niyato, S. Zhang, Privacy-preserving traffic flow prediction: a federated learning approach. IEEE Internet Things J., 1–1 (2020)

    Google Scholar 

  30. L.U. Khan, N.H. Tran, S.R. Pandey, W. Saad, Z. Han, M.N. Nguyen, C.S. Hong, Federated learning for edge networks: Resource optimization and incentive mechanism. arXiv preprint arXiv, 1911.05642 (2019)

    Google Scholar 

  31. J. Kang, Z. Xiong, D. Niyato, H. Yu, Y.-C. Liang, D.I. Kim, Incentive design for efficient federated learning in mobile networks: A contract theory approach, in 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS), (IEEE, 2019), pp. 1–5

    Google Scholar 

  32. E. Jeong, S. Oh, H. Kim, J. Park, M. Bennis, S.-L. Kim, Communication-efficient on-device machine learning: Federated dis- tillation and augmentation under non-iid private data. arXiv preprint arXiv, 1811.11479 (2018)

    Google Scholar 

  33. N.H. Tran, W. Bao, A. Zomaya, N.M. NH, C.S. Hong, Federated learning over wireless networks: Optimization model design and analysis, in IEEE INFOCOM 2019-IEEE Conference on Computer Communications, (IEEE, 2019), pp. 1387–1395

    Chapter  Google Scholar 

  34. K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C.M. Kiddon, J. Konen, S. Mazzocchi, B. McMahan, T.V. Overveldt, D. Petrou, D. Ramage, J. Roselander, Towards federated learning at scale: System design, in SysML 2019, 2019, to appear. [Online]. Available: https://arxiv.org/abs/1902.01046

  35. A.F. Atiya, A.G. Parlos, New results on recurrent network training: Unifying the algorithms and accelerating convergence. IEEE Trans. Neural Netw. 11(3), 697–709 (2000)

    Article  Google Scholar 

  36. Z. Wang, M. Song, Z. Zhang, Y. Song, Q. Wang, H. Qi, Beyond inferring class representatives: User-level privacy leakage from federated learning, in IEEE INFOCOM 2019-IEEE Conference on Computer Communications, (IEEE, 2019), pp. 2512–2520

    Chapter  Google Scholar 

  37. L. Zhu, Z. Liu, S. Han, Deep leakage from gradients, in Advances in neural information processing systems, (2019), pp. 14 747–14 756

    Google Scholar 

  38. L. Li, H. Xiong, Z. Guo, J. Wang, C.-Z. Xu, Smartpc: Hierarchical pace control in real-time federated learning system, in 2019 IEEE Real- Time Systems Symposium (RTSS), (IEEE, 2019), pp. 406–418

    Chapter  Google Scholar 

  39. A. Portnoy D. Hendler, Towards realistic byzantine-robust federated learning. arXiv preprint arXiv, 2004.04986 (2020)

    Google Scholar 

  40. S. Guo, T. Zhang, X. Xie, L. Ma, T. Xiang, Y. Liu, Towards byzantine-resilient learning in decentralized systems. arXiv preprint arXiv, 2002.08569 (2020)

    Google Scholar 

  41. F. Ang, L. Chen, N. Zhao, Y. Chen, W. Wang, F.R. Yu, Robust Federated Learning with Noisy Communication (IEEE Transactions on Communications, 2020)

    Book  Google Scholar 

  42. S.P. Yadav, K.K. Agrawal, B.S. Bhati, et al., Blockchain-based cryptocurrency regulation: an overview. Comput. Econ. (2020). https://doi.org/10.1007/s10614-020-10050

  43. Y. Huang, Y. Su, S. Ravi, Z. Song, S. Arora, K. Li, Privacy-preserving learning via deep net pruning. arXiv preprint arXiv, 2003.01876 (2020)

    Google Scholar 

  44. T.-D. Cao, T. Truong-Huu, H. Tran, K. Tran, A federated learning framework for privacy-preserving and parallel training, arXiv preprint arXiv, 2001.09782 (2020)

    Google Scholar 

  45. Z. Jiang, A. Balu, C. Hegde, S. Sarkar, Collaborative deep learning in fixed topology networks, in Advances in Neural Information Processing Systems, (2017), pp. 5904–5914

    Google Scholar 

  46. L.U. Khan, N.H. Tran, S.R. Pandey, W. Saad, Z. Han, M.N. Nguyen, C.S. Hong, Federated learning for edge networks: resource optimization and incentive mechanism. arXiv preprint arXiv, 1911.05642 (2019)

    Google Scholar 

  47. J. Weng, J. Weng, J. Zhang, M. Li, Y. Zhang, W. Luo, Deepchain: Auditable and Privacy-Preserving Deep Learning with Blockchain-Based Incentive (IEEE Transactions on Dependable and Secure Computing, 2019)

    Google Scholar 

  48. Y. Zhan, P. Li, Z. Qu, D. Zeng, S. Guo, A Learning-Based Incentive Mechanism for Federated Learning (IEEE Internet of Things Journal, 2020)

    Book  Google Scholar 

  49. H. Yu, Z. Liu, Y. Liu, T. Chen, M. Cong, X. Weng, D. Niyato, Q. Yang, A fairness-aware incentive scheme for federated learning, in Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society,

    Google Scholar 

  50. A. Fallah, A. Mokhtari, A. Ozdaglar, Personalized federated learning: A meta-learning approach. arXiv preprint arXiv, 2002.07948 (2020)

    Google Scholar 

  51. Q. Wu, K. He, X. Chen, Personalized federated learning for intelligent IoT applications: a cloud-edge based framework. IEEE Open J. Comput. Soc., 1–1 (2020)

    Google Scholar 

  52. R. Hu, Y. Guo, H. Li, Q. Pei, Y. Gong, Personalized Federated Learning with Differential Privacy (IEEE Internet of Things Journal, 2020)

    Book  Google Scholar 

  53. V. Kulkarni, M. Kulkarni, A. Pant, Survey of personalization techniques for federated learning. arXiv preprint arXiv, 2003.08673 (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. IT Department, ABES Institute of Technology, Ghaziabad, UP, India

    Kaushal Kishor

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  1. Kaushal Kishor
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Correspondence to Kaushal Kishor .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, G.L. Bajaj Institute of Technology and Management (GLBITM) Affiliated to AKTU, Lucknow, Uttar Pradesh, India

    Satya Prakash Yadav

  2. Department of Computer Science and Engineering, Chandigarh University, Mohali, India

    Bhoopesh Singh Bhati

  3. National Institute of Technology Hamirpur, Himachal Pradesh, India

    Dharmendra Prasad Mahato

  4. Kyungpook National University, Daegu, Korea (Republic of)

    Sachin Kumar

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Kishor, K. (2022). Communication-Efficient Federated Learning. In: Yadav, S.P., Bhati, B.S., Mahato, D.P., Kumar, S. (eds) Federated Learning for IoT Applications. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-85559-8_9

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