Article Title: A Unified Framework for Enhancing Federated Learning Security and Robustness Using Generative Adversarial Networks Blockchain and Differential Privacy
Volume Number: 1
Issue: 1
Year: 2025
Article Type: Original Article
Author Names: Shehryar Qamar Paracha¹*, Muhammad Inam ul Haq¹, Rana Ali Sher Haide², Adil Khan³, Nageeta kumari⁴, Hafiza Tahira Farzand⁵, Khaleeq Uddin⁶, and Habib Ur Rehman⁷
Page Number: 1-10
Affiliations: ¹Department of Electronic Engineering, The Islamia University of Bahawalpur, Pakistan; ²Department of Mathematics, COMSATS University, Pakistan; ³Department of Computer Science, Abasyn University, Pakistan; ⁴Department of Software Project Management, National University of Computer and Emerging Science (FAST-NUCES), Pakistan; ⁵Department of Electronic Engineering, The Islamia University of Bahawalpur, Pakistan; ⁶Department of Computer Science, University of the Punjab, Pakistan; ⁷Department of Computer Science, University of Karachi, Pakistan

*Correspondence: Mr. Shehryar Qamar Paracha, Department of Electronic Engineering, The Islamia University of Bahawalpur, Pakistan, Email Address: shehryar.paracha@iub.edu.pk
Keywords: Federated Learning; Generative Adversarial Networks; Blockchain Technology; Differential Privacy; Adversarial Robustness; Secure Decentralized Systems
Abstract: To define the approach taken by federated learning AI models more
distinctly, FL enables sharing the model across the range of networks without re
vealing sensitive data. The key problem, however, is that this model does not pro
vide a centralized authority for data control and as such is prone to adversarial
and data poisoning attacks. This problem leads to a compromise between the
model accuracy and the model security. In solving these problems, we suggest a
framework for federated learning GAN-augmented with blockchain and differ
ential privacy. The GAN module is responsible for performing adversarial attacks
on the AI model which gives enables any real-world model to be robust to true
attacks. GANs also assist in detecting poisoned updates and as such enables se
curity improvement. Blockchain guarantees trust and transparency between the
participating clients and differential privacy provides confidential data collection
for effective counter measures to privacy breaches. The effectiveness of the frame
work has been verified experimentally and it has been proven that the integrated
framework successfully reduced the success rate of the attacks against it to 8.0%,
increased the detection accuracy to 98.0% and maintained the model accuracy at
the level of 95.0%. It is now evident that due to the integration of GANs, Block
chain and differential privacy FL is challenging to be combated against in a malicious manner without compromising the target model performance significantly.
The exceptional detection accuracy ratio guarantees that any corrupted updates
will be captured before doing so much damage that it alters the e-learning pro
cess. This necessary balance of model performance and trust with high detection
rate enables new GAN, blockchain, and differential privacy model to be used in
protecting the learning process. This novel capability will help alleviate privacy
concerns in critical settings like healthcare, finance, and cybersecurity, where in
formation is central to improving outcomes.
References:

[1] McMahan B, Moore E, Ramage D, et al. Communication-efficient learning of deep networks from decentralized data. Proc 20th Int Conf Artificial Intelligence and Statistics (AISTATS). 2017;1273–1282.
[2] Bhavsingh M, Samunnisa K, Pannalal B. A blockchain-based approach for securing network communications in IoT environments. Int J Comput Eng Res Trends. 2023;10(10):37–43.
[3] Zhang Z, Luo W, Jiang P, et al. Blockchain-based federated learning for device failure detection in industrial IoT. IEEE Internet Things J. 2021;8(6):4418–4427.
[4] Yin H, Chen R, Tang Z, et al. A blockchain-based federated learning framework for secure data sharing in industrial IoT. IEEE Trans Ind Informat. 2021;17(7):4718–4726.
[5] Kunna Azrag MA, Shareef SKK, Ann J, et al. A novel blockchain-based framework for enhancing supply chain management. Int J Comput Eng Res Trends. 2023;10(6):22–28.
[6] Papernot N, McDaniel P, Sinha A, et al. Towards the science of security and privacy in machine learning. Proc IEEE European Symp Security and Privacy (EuroS&P). 2018;399–414.
[7] Shokri R, Shmatikov V. Privacy-preserving deep learning. Proc ACM SIGSAC Conf Computer and Communications Security (CCS). 2015;1310–1321.
[8] Abadi M, Chu A, Goodfellow I, et al. Deep learning with differential privacy. Proc ACM SIGSAC Conf Computer and Communications Security (CCS). 2016;308–318.
[9] Hitaj L, Ateniese G, Perez-Cruz F. Deep models under the GAN: Information leakage from collaborative deep learning. Proc ACM SIGSAC Conf Computer and Communications Security (CCS). 2017;603–618.
[10] Li T, Sahu AK, Talwalkar A, et al. Federated learning: Challenges, methods, and future directions. IEEE Signal Process Mag. 2020;37(3):50–60.
[11] Yang Q, Liu Y, Chen T, et al. Federated machine learning: Concept and applications. ACM Trans Intell Syst Technol. 2019;10(2):1–19.
[12] Zhao Y, Hu R, Zhang Z. A comprehensive survey on differential privacy for deep learning. IEEE Access. 2021;9:153166153191.
[13] McMahan HB, Moore E, Ramage D, et al. Federated learning of deep networks using model averaging. Google Research Blog. 2017.
[14] Bonawitz K, McMahan HB, Hsu E, et al. Federated learning: Collaborative machine learning without centralized training data. Google AI. 2017.
[15] Ren J, Yu G, He S, et al. Secure and privacy-preserving federated learning for healthcare: A survey. IEEE Access. 2021;9:112066–112087.
[16] Pathak R, Rane S. Differential privacy in machine learning: An overview. Proc Int Conf Advances in Computing and Communication Engineering (ICACCE). 2020;1–8.
[17] Reddy LM, Doss S. Turbocharging blockchain: Cutting-edge load balancing for split-join architecture. Int J Comput Eng Res Trends. 2024;11(5):9–23.
[18] Mironov I. Renyi differential privacy. Proc IEEE Comput Security Found Symp (CSF). 2017;263–275.
[19] Sun K, Sahu AK, Yang Z, et al. Robust federated learning through collaborative machine teaching. Proc Int Conf Learning Representations (ICLR). 2021;1–15.
[20] Roy AG, Siddiqui S, Breckon TP. Generative adversarial networks: A survey on the state of the art and future directions. IEEE Access. 2021;9:153034–153054.
[21] Li C, He Z, Xue Y, et al. Blockchain for federated learning toward secure distributed machine learning systems: A survey. Comput Commun. 2021;171:139–152.
[22] Zhang F, Zhang H, Wu S. Blockchain-based federated learning for decentralized training. IEEE Trans Netw Service Manag. 2021;18(4):4836–4847.
[23] Kang Y, Liu X, Guo L, et al. Federated learning and differential privacy: A survey on privacy-preserving collaborative machine learning. IEEE Access. 2022;10:20850–20871.
[24] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Proc Advances in Neural Information Processing Systems (NeurIPS). 2014;2672–2680.
[25] Akoguhi NP, Bhavsingh M. Blockchain technology in real estate: Applications, challenges, and future prospects. Int J Com put Eng Res Trends. 2023;10(9):16–21.