As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a paradigm shift by combining privacy preserving training with efficient, on device computation. This paper introduces a cutting-edge FL-edge integration framework, achieving a 10% to 15% increase in model accuracy and reducing communication costs by 25% in heterogeneous environments. Blockchain based secure aggregation ensures robust and tamper-proof model updates, while exploratory quantum AI techniques enhance computational efficiency. By addressing key challenges such as device variability and non-IID data, this work sets the stage for the next generation of adaptive, privacy-first AI systems, with applications in IoT, healthcare, and autonomous systems.
References
[1]
Brecko, A., Kajati, E., Koziorek, J. and Zolotova, I. (2022) Federated Learning for Edge Computing: A Survey. AppliedSciences, 12, Article 9124. https://doi.org/10.3390/app12189124
[2]
Myakala, P.K., Bura, C. and Jonnalagadda, A.K. (2024) Federated Learning and Data Privacy: A Review of Challenges and Opportunities. InternationalJournalofResearchPublicationandReviews, 5, 1867-1879. https://doi.org/10.55248/gengpi.5.1224.3512
[3]
Wu, L., Ruan, W., Hu, J. and He, Y. (2023) A Survey on Blockchain-Based Federated Learning. FutureInternet, 15, Article 400. https://doi.org/10.3390/fi15120400
[4]
Gill, S.S., Golec, M., Hu, J., Xu, M., Du, J., Wu, H., etal. (2024) Edge AI: A Taxonomy, Systematic Review and Future Directions. ClusterComputing, 28, Article No. 18. https://doi.org/10.1007/s10586-024-04686-y
[5]
Tiwari, R., and Gupta, S. (2022) Federated Continual Learning for Edge-AI: A Com-prehensive Survey, arXiv: 2411.13740. https://doi.org/10.48550/arXiv.2411.13740
[6]
Ning, W., Zhu, Y., Song, C., Li, H., Zhu, L., Xie, J., etal. (2024) Blockchain-Based Federated Learning: A Survey and New Perspectives. AppliedSciences, 14, Article 9459. https://doi.org/10.3390/app14209459
[7]
Qiao, C., Li, M., Liu, Y. and Tian, Z. (2024) Transitioning from Federated Learning to Quantum Federated Learning in Internet of Things: A Comprehensive Survey. IEEECommunicationsSurveys&Tutorials. https://doi.org/10.1109/comst.2024.3399612
[8]
Kalapaaking, A.P., Khalil, I., Rahman, M.S., Atiquzzaman, M., Yi, X. and Almashor, M. (2023) Blockchain-Based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things. IEEETransactionsonIndustrialInformatics, 19, 1703-1714. https://doi.org/10.1109/tii.2022.3170348
[9]
Prigent, C., Costan, A., Antoniu, G. and Cudennec, L. (2024) Enabling Federated Learning across the Computing Continuum: Systems, Challenges and Future Directions. FutureGenerationComputerSystems, 160, 767-783. https://doi.org/10.1016/j.future.2024.06.043
[10]
Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A. and Smith, V. (2020) Federated Optimization in Heterogeneous Networks. Proceedings of Machine Learning and Systems. https://proceedings.mlsys.org/paper_files/paper/2020/file/1f5fe83998a09396ebe6477d9475ba0c-Paper.pdf
[11]
Karimireddy, S., Kale, S., and Reddi, S. (2020) SCAFFOLD: Stochastic Controlled Averaging for Federated Learning, Proceedings of the 37th International Conference on Machine Learning, Online, 13-18 July 2020, 5132-5143. https://proceedings.mlr.press/v119/karimireddy20a.html
[12]
Fukami, T., Murata, T., Niwa, K. and Tyou, I. (2024) DP-Norm: Differential Privacy Primal-Dual Algorithm for Decentralized Federated Learning. IEEETransactionsonInformationForensicsandSecurity, 19, 5783-5797. https://doi.org/10.1109/tifs.2024.3390993
[13]
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., etal. (2017) Practical Secure Aggregation for Privacy-Preserving Machine Learning. Proceedingsofthe 2017 ACMSIGSACConferenceonComputerandCommunicationsSecurity, Dallas, 30 October-3 November 2017, 1175-1191. https://doi.org/10.1145/3133956.3133982
[14]
Tan, A.Z., Yu, H., Cui, L. and Yang, Q. (2023) Towards Personalized Federated Learning. IEEETransactionsonNeuralNetworksandLearningSystems, 34, 9587-9603. https://doi.org/10.1109/tnnls.2022.3160699
[15]
Mnif, M., Sahnoun, S., Ben Saad, Y., Fakhfakh, A. and Kanoun, O. (2024) Combinative Model Compression Approach for Enhancing 1D CNN Efficiency for Eit-Based Hand Gesture Recognition on IoT Edge Devices. Internet of Things, 28, Article 101403. https://doi.org/10.1016/j.iot.2024.101403
[16]
Edge AI and Computing Unit 7-Hardware Accelerators for Edge AI. https://library.fiveable.me/edge-ai-and-computing/unit-7
[17]
Li, H., Sun, M., Xia, F., Xu, X. and Bilal, M. (2024) A Survey of Edge Caching: Key Issues and Challenges. TsinghuaScienceandTechnology, 29, 818-842. https://doi.org/10.26599/tst.2023.9010051
[18]
Gurung, D., Pokhrel, S.R. and Li, G. (2023) Quantum Federated Learning: Analysis, Design and Implementation Challenges. arXiv: 2306.15708. https://doi.org/10.48550/arXiv.2306.15708
[19]
Zhang, Y., Lu, Y. and Liu, F. (2023) A Systematic Survey for Differential Privacy Techniques in Federated Learning. JournalofInformationSecurity, 14, 111-135. https://doi.org/10.4236/jis.2023.142008
