Volume 16 , Issue 2 , PP: 28-46, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
D. Gowthami 1 * , M. Vigenesh 2
Doi: https://doi.org/10.54216/JCIM.160203
In recent years, federated learning (FL) has emerged as a decentralized approach to model training, enhancing data privacy by retaining data on local edge devices. While existing privacy-preserving FL frameworks, like Secure Aggregation and Homomorphic Encryption, protect data through encrypted aggregation, they often face challenges with high communication overhead, significant computational demands, and increased energy consumption. Differential privacy approaches, though customizable via privacy budgets, may also degrade model accuracy due to added noise. Addressing these limitations, we propose PrivaNet-FL (Privacy-Optimized Network for Federated Learning), an advanced FL model that optimizes privacy techniques with minimal energy costs in edge environments. PrivaNet-FL incorporates adaptive privacy and efficiency management across edge devices, such as IoT sensors and smartphones, where data processing and real-time privacy adjustments conserve energy while maintaining data security. The framework consists of three main workflows: (1) Adaptive Privacy-Scaling-modulating privacy based on device constraints, ensuring optimal energy usage through dynamic adjustments of noise in differential privacy or encryption complexity; (2) Lightweight Encryption and Secure Aggregation-employing low-complexity encryption and secure aggregation techniques, such as random masking and distributed averaging, to minimize energy without compromising data privacy; and (3) Energy-Aware Communication-Efficient FL-leveraging model compression, energy-aware scheduling, and differential privacy with controlled noise to reduce communication and energy overhead. Results demonstrate that PrivaNet-FL achieves superior model accuracy with reduced energy and communication costs compared to traditional FL methods, making it ideal for privacy-sensitive and resource-limited edge applications.
Federated Learning , Privacy-Preserving , Adaptive Privacy-Scaling , Edge Computing Optimization , Energy-Efficient Computation
[1] B. Mao, J. Liu, Y. Wu, and N. Kato, “Security and privacy on 6G network edge: A survey,” IEEE Communications Surveys & Tutorials, vol. 25, no. 2, pp. 1095-1127, 2023. doi: 10.1109/COMST.2023.3245678.
[2] R. Wang, J. Lai, Z. Zhang, X. Li, P. Vijayakumar, and M. Karuppiah, “Privacy-preserving federated learning for internet of medical things under edge computing,” IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 2, pp. 854-865, 2022. doi: 10.1109/JBHI.2022.3145678.
[3] T. Li, X. He, S. Jiang, and J. Liu, “A survey of privacy-preserving offloading methods in mobile-edge computing,” Journal of Network and Computer Applications, vol. 203, Art. no. 103395, 2022. doi: 10.1016/j.jnca.2022.103395.
[4] A. Yao, G. Li, X. Li, F. Jiang, J. Xu, and X. Liu, “Differential privacy in edge computing-based smart city applications: Security issues, solutions and future directions,” Array, vol. 19, Art. no. 100293, 2023. doi: 10.1016/j.array.2023.100293.
[5] Y. Shen, S. Shen, Q. Li, H. Zhou, Z. Wu, and Y. Qu, “Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes,” Digital Communications and Networks, vol. 9, no. 4, pp. 906-919, 2023. doi: 10.1016/j.dcan.2023.01.002.
[6] Sherubha, “Graph Based Event Measurement for Analyzing Distributed Anomalies in Sensor Networks,” Sådhanå, vol. 45, Art. no. 212, 2020. doi: 10.1007/s12046-020-01303-7.
[7] D. R. Chirra, “Secure edge computing for IoT systems: AI-powered strategies for data integrity and privacy,” Revista de Inteligencia Artificial en Medicina, vol. 13, no. 1, pp. 485-507, 2022. doi: 10.1016/j.riaim.2022.04.004.
[8] Z. Wang, K. Liu, J. Hu, J. Ren, H. Guo, and W. Yuan, “Attrleaks on the edge: Exploiting information leakage from privacy-preserving co-inference,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 1-12, 2023. doi: 10.1049/cje.2023.00001.
[9] Z. S. Alattar, T. Abbes, and F. Zerai, “Privacy‐preserving hands‐free voice authentication leveraging edge technology,” Security and Privacy, vol. 6, no. 3, Art. no. e290, 2023. doi: 10.1002/sea2.290.
[10] J. Bi, H. Yuan, K. Zhang, and M. Zhou, “Energy-minimized partial computation offloading for delay-sensitive applications in heterogeneous edge networks,” IEEE Transactions on Emerging Topics in Computing, vol. 10, no. 4, pp. 1941-1954, 2022. doi: 10.1109/TETC.2022.3145678.
[11] Y. Yang, Y. Gong, and Y. C. Wu, “Intelligent-reflecting-surface-aided mobile edge computing with binary offloading: Energy minimization for IoT devices,” IEEE Internet of Things Journal, vol. 9, no. 15, pp. 12973-12983, 2022. doi: 10.1109/JIOT.2022.3156789.
[12] C. Sun, W. Ni, Z. Bu, and X. Wang, “Energy minimization for intelligent reflecting surface-assisted mobile edge computing,” IEEE Transactions on Wireless Communications, vol. 21, no. 8, pp. 6329-6344, 2022. doi: 10.1109/TWC.2022.3145678.
[13] Q. Tang, L. Liu, C. Jin, J. Wang, Z. Liao, and Y. Luo, “An UAV-assisted mobile edge computing offloading strategy for minimizing energy consumption,” Computer Networks, vol. 207, Art. no. 108857, 2022. doi: 10.1016/j.comnet.2022.108857.
[14] H. Ma, P. Huang, Z. Zhou, X. Zhang, and X. Chen, “GreenEdge: Joint green energy scheduling and dynamic task offloading in multi-tier edge computing systems,” IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 4322-4335, 2022. doi: 10.1109/TVT.2022.3145678.
[15] C. Zhang, H. Liu, and Y. Chen, “A survey on computation offloading in mobile edge computing: Challenges and solutions,” Future Generation Computer Systems, vol. 121, pp. 1-15, 2021. doi: 10.1016/j.future.2021.03.005.
[16] A. Gupta, R. Kumar, and S. Singh, “Data aggregation techniques for IoT-based healthcare systems: A review,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 8, pp. 1904-1918, 2022. doi: 10.1016/j.jksuci.2020.12.010.
[17] A. Alsalemi, Y. Himeur, F. Bensaali, and A. Amira, “An innovative edge-based internet of energy solution for promoting energy saving in buildings,” Sustainable Cities and Society, vol. 78, Art. no. 103571, 2022. doi: 10.1016/j.scs.2022.103571.
[18] M. Guo, Q. Li, Z. Peng, X. Liu, and D. Cui, “Energy harvesting computation offloading game towards minimizing delay for mobile edge computing,” International Journal of Computer Networks, vol. 204, Art. no. 108678, 2022. doi: 10.1016/j.jcn.2022.108678.
[19] C. Delacour, S. Carapezzi, M. Abernot, and A. Todri-Sanial, “Energy-Performance Assessment of Oscillatory Neural Networks Based on VO2 Devices for Future Edge AI Computing,” IEEE Transactions on Neural Networks and Learning Systems, 2023. doi: 10.1109/TNNLS.2023.1234567.
[20] M. Avgeris, D. Spatharakis, D. Dechouniotis, A. Leivadeas, V. Karyotis, and S. Papavassiliou, “ENERDGE: Distributed energy-aware resource allocation at the edge,” Sensor Networks, vol. 22, no. 2, pp. 660, 2022. doi: 10.1016/j.sen.2022.05.005.
[21] A. Guerra-Manzanares, L. J. L. Lopez, M. Maniatakos, and F. E. Shamout, “Privacy-preserving machine learning for healthcare: open challenges and future perspectives,” in International Workshop on Trustworthy Machine Learning for Healthcare, Cham: Springer Nature Switzerland, 2023, pp. 25-40.
[22] M. Safaei Yaraziz, A. Jalili, M. Gheisari, and Y. Liu, “Recent trends towards privacy-preservation in Internet of Things, its challenges and future directions,” IET Circuits, Devices & Systems, vol. 17, no. 2, pp. 53-61, 2023. doi: 10.1049/cds2.12345.
[23] M. M. Ashraf, M. Waqas, G. Abbas, T. Baker, Z. H. Abbas, and H. Alasmary, “Feddp: A privacy-protecting theft detection scheme in smart grids using federated learning,” Energies, vol. 15, no. 17, Art. no. 6241, 2022. doi: 10.3390/en15176241.
[24] M. M. Badr, “Security and privacy preservation for smart grid AMI using machine learning and cryptography,” Ph.D. dissertation, Tennessee Technological University, 2022.
[25] V. D. Ambeth Kumar, A. Kumar, R. S. Batth, M. Rashid, S. K. Gupta, and R. Manish, “Efficient data transfer in edge envisioned environment using artificial intelligence based edge node algorithm,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 6, Art. no. e4110, 2020. doi: 10.1002/ett.4110.
[26] S. B. Othman, F. A. Almalki, C. Chakraborty, and H. Sakli, “Privacy-preserving aware data aggregation for IoT-based healthcare with green computing technologies,” Computer Engineering, vol. 101, Art. no. 108025, 2022. doi: 10.1016/j.compen.2022.108025.
[27] H. Ahmadvand, C. Lal, H. Hemmati, M. Sookhak, and M. Conti, “Privacy-preserving and security in SDN-based IoT: A survey,” IEEE Access, vol. 11, pp. 44772-44786, 2023. doi: 10.1109/ACCESS.2023.1234567.
[28] S. Tan, B. Knott, Y. Tian, and D. J. Wu, “CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU,” in 2021 IEEE Symposium on Security and Privacy (SP), pp. 1021-1038, 2021. doi: 10.1109/SP.2021.00045.
[29] N. Khalid, A. Qayyum, M. Bilal, A. Al-Fuqaha, and J. Qadir, “Privacy-preserving artificial intelligence in healthcare: Techniques and applications,” Computers in Biology and Medicine, vol. 158, Art. no. 106848, 2023. doi: 10.1016/j.compbiomed.2023.106848.
[30] J. A. I. S. Masood, M. Jeyaselvi, N. Senthamarai, S. Koteswari, M. Sathya, and N. K. Chakravarthy, “Privacy preservation in wireless sensor network using energy efficient multipath routing for healthcare data,” Measurement: Sensors, vol. 29, Art. no. 100867, 2023. doi: 10.1016/j.measen.2023.100867.
