Revolutionizing Smart Devices: Integrating Federated Learning with IoT for Advanced Digital Innovation
Keywords:
Federated Learning, Internet of Things (IoT), Smart Devices, Data Privacy, Model AggregationAbstract
The explosive growth of the Internet of Things (IoT) has transformed the capabilities of smart devices, allowing them to gather and process enormous quantities of data. But as the amount of data grows, serious issues with privacy, security, and bandwidth use surface. Federated Learning (FL), which reduces data transfer, improves privacy, and allows smart devices to work together to create shared models while storing all training data locally, provides a novel solution to these problems. This study looks at the IoT and Federated Learning synergy and shows how it might lead to more advanced digital innovation in smart devices. Through real-time data analysis and localized modifications made by smart devices, FL enhances service efficiency and customisation while protecting customer security and privacy. The suggested solution is assessed in multiple situations, demonstrating its applicability in sectors such as smart homes, healthcare, and industrial IoT. This creative approach will usher in a new era of intelligent transformation that will improve the security, autonomy, and user experience of smart devices
References
W. Y. B. Lim, J. Huang, Z. Xiong, J. Kang, D. Niyato, X.-S. Hua, C. Leung, C. Miao, Towards federated learning in uav-enabled internet of vehicles: A multi-dimensional contract-matching approach, IEEE Trans-actions on Intelligent Transportation Systems (2021)
S. R. Pokhrel, J. Choi, Federated learning with blockchain for autonomous vehicles: Analysis and design challenges, IEEE Transactions on Communications 68 (8) (2020) 4734–4746
J. Koneˇcn`y, H. B. McMahan, D. Ramage, P. Richt´arik, Federated optimization: Distributed machine learning for on-device intelligence, arXiv preprint arXiv:1610.02527 (2016)
M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, S. Cui, A joint learning and communications framework for federated learning over wireless networks, IEEE Transactions on Wireless Communications 20 (1) (2020) 269–283
J.-J. Yang, J.-Q. Li, Y. Niu, A hybrid solution for privacy-preserving medical data sharing in the cloud environment, Future Generation computer systems 43 (2015) 74–86
S. Sharma, C. Xing, Y. Liu, Y. Kang, Secure and efficient federated transfer learning, in: 2019 IEEE International Conference on Big Data (Big Data), IEEE, 2019, pp. 2569–2576
B. Xu, W. Xia, J. Zhang, T. Q. Quek, H. Zhu, Online client scheduling for fast federated learning, IEEE Wireless Communications Letters (2021). 8. W. Xia, T. Q. Quek, K. Guo, W. Wen, H. H. Yang, H. Zhu, Multi-armed bandit-based client scheduling for federated learning, IEEE Transactions on Wireless Communications 19 (11) (2020) 7108–7123
S. Luo, X. Chen, Q. Wu, Z. Zhou, S. Yu, Hfel: Joint edge association and resource allocation for cost-efficient hierarchical federated edge learning, IEEE Transactions on Wireless Communications 19 (10) (2020) 6535–6548
H. H. Yang, Z. Liu, T. Q. Quek, H. V. Poor, Scheduling policies for federated learning in wireless net-works, IEEE Transactions on Communications 68 (1) (2019) 317–333
J. Xu, H. Wang, L. Chen, Bandwidth allocation for multiple federated learning services in wireless edge networks, arXiv preprint arXiv:2101.03627 (2021). 12. V. Tolpegin, S. Truex, M. E. Gursoy, L. Liu, Data poisoning attacks against federated learning systems, in: European Symposium on Research in Computer Security, Springer, 2020, pp. 480–501
M. Fang, X. Cao, J. Jia, N. Gong, Local model poisoning attacks to byzantine robust federated learning, in: 29th {USENIX} Security Symposium ({USENIX} Security 20), 2020, pp. 1605–1622
Smith, J., et al. "Advanced Federated Learning Algorithms for Large-Scale IoT Networks." Journal of Internet of Things 6.2 (2023): 123-135
V. Mothukuri, R. M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, G. Srivastava, A survey on security and privacy of federated learning, Future Generation Computer Systems 115 (2021) 619–640
Li, Z., et al. "Privacy-Preserving Federated Learning for IoT: Applications in Smart Agriculture and Transportation." IEEE Transactions on Industrial Informatics (2023)
Wang, Q., et al. "Enhancing Energy Management and Privacy in Smart Homes using Federated Learning." In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys), 2023
Cheng, X., et al. "New Approaches for Federated Learning in Healthcare and Industrial IoT." IEEE Transactions on Industrial Electronics (2023)
L. Zhu, S. Han, Deep leakage from gradients, in: Federated learning, Springer, 2020, pp. 17–31
M. Nasr, R. Shokri, A. Houmansadr, Machine learning with membership privacy using adversarial regularization, in: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018, pp. 634–646
M. Wu, D. Ye, J. Ding, Y. Guo, R. Yu, M. Pan, Incentivizing differentially private federated learning: A multi-dimensional contract approach, IEEE Internet of Things Journal (2021)
J. Zhao, X. Zhu, J. Wang, J. Xiao, Efficient client contribution evaluation for horizontal federated learn-ing, in: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2021, pp. 3060–3064
O. Choudhury, A. Gkoulalas-Divanis, T. Salonidis, I. Sylla, Y. Park, G. Hsu, A. Das, Differential priva-cy-enabled federated learning for sensitive health data, arXiv preprint arXiv:1910.02578 (2019)
Q. Wu, X. Chen, Z. Zhou, J. Zhang, Fedhome: Cloud-edge based personalized federated learning for in-home health monitoring, IEEE Transactions on Mobile Computing (2020)
K. Wei, J. Li, M. Ding, C. Ma, H. H. Yang, F. Farokhi, S. Jin, T. Q. Quek, H. V. Poor, Federated learning with differential privacy: Algorithms and performance analysis, IEEE Transactions on Information Forensics and Security 15 (2020) 3454– 3469
T. S. Brisimi, R. Chen, T. Mela, A. Olshevsky, I. C. Paschalidis, W. Shi, Federated learning of predictive models from federated electronic health records, international journal of medical informatics 112 (2018) 59–67
D. Liu, T. Miller, R. Sayeed, K. D. Mandl, Fadl: Federated-autonomous deep learning for distributed electronic health record, arXiv preprint arXiv:1811.11400 (2018)
L. Huang, A. L. Shea, H. Qian, A. Masurkar, H. Deng, D. Liu, Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records, Journal of biomedical informatics 99 (2019) 103291
L. Huang, Y. Yin, Z. Fu, S. Zhang, H. Deng, D. Liu, Loadaboost: Loss-based adaboost federated machine learning with reduced computational complexity on iid and non-iid intensive care data, Plops one 15 (4) (2020) e0230706
X. Tan, C.-C. H. Chang, L. Tang, A tree-based federated learning approach for personalized treatment effect estimation from heterogeneous data sources, arXiv preprint arXiv:2103.06261 (2021)
Z. Yan, J. Wicaksana, Z. Wang, X. Yang, K.-T. Cheng, Variation-aware feder ated learning with multisource decentralized medical image data, IEEE Journal of Biomedical and Health Informatics (2020)
W. Li, F. Milletar`ı, D. Xu, N. Rieke, J. Hancox, W. Zhu, M. Baust, Y. Cheng, S. Ourselin, M. J. Cardoso, et al., Privacy-preserving federated brain tumour segmentation, in: International workshop on machine learning in medical imaging, Springer, 2019, pp. 133–141
S. Silva, B. A. Gutman, E. Romero, P. M. Thompson, A. Altmann, M. Lorenzi, Federated learning in distributed medical databases: Meta-analysis of large-scale subcortical brain data, in: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), IEEE, 2019, pp. 270–274
Q.-V. Pham, D. C. Nguyen, T. Huynh-The, W.-J. Hwang, P. N. Pathirana, Artificial intelligence (ai) and big data for coronavirus (covid-19) pandemic: A survey on the state-of-the-arts (2020)
M. Loey, F. Smarandache, N. E. M Khalifa, Within the lack of chest covid-19 x-ray dataset: a novel de-tection model based on gain and deep transfer learning, Symmetry 12 (4) (2020) 651.
