Privacy-Preserving Email Spam Detection using Federated Learning
DOI:
https://doi.org/10.15662/IJEETR.2026.0802029Keywords:
Email Spam Detection, Federated Learning, Privacy Preservation, Distributed Machine Learning, Natural Language Processing, Secure AggregationAbstract
The rapid growth of electronic communication has led to an increased prevalence of email spam, posing significant challenges to user privacy and cybersecurity. Traditional centralized spam detection approaches require collecting large volumes of user data on central servers, raising serious privacy concerns and regulatory issues.
To address these challenges, this study proposes a privacy-preserving email spam detection framework based on federated learning. The proposed method enables multiple clients to collaboratively train a global machine learning model without sharing raw email data, thereby ensuring data confidentiality.
In this approach, local models are trained on decentralized datasets residing on user devices, and only model updates are transmitted to a central server for aggregation. To further enhance privacy, techniques such as secure aggregation and differential privacy are incorporated to prevent leakage of sensitive information from model parameters. The system employs natural language processing techniques for feature extraction and utilizes classification algorithms to distinguish between spam and legitimate emails.
Experimental results demonstrate that the federated learning-based model achieves competitive accuracy compared to traditional centralized methods while significantly reducing privacy risks. Additionally, the framework shows robustness against data heterogeneity and scalability across multiple clients. The proposed solution highlights the potential of federated learning as an effective and privacy-aware paradigm for real-world spam detection systems.
References
1. H. Brendan McMahan et al., “Communication-Efficient Learning of Deep Networks from Decentralized Data,”AISTATS,2017.
2. J. Konečný et al., “Federated Learning: Strategies for Improving Communication Efficiency,” 2016.
3. I.Goodfellowetal. Deep Learning MIT Press, 2016.
4. T. Yang et al., “Applied Federated Learning: Improving Google Keyboard Query Suggestions,” 2018.
5. A. Shokri and V. Shmatikov, “Privacy-Preserving Deep Learning,” CCS, 2015.
6. “Federated Learning-Based Spam Detection Using Feature Fusion,” IEEE Access, 2024.
7. “Privacy-Preserving Federated Learning for Phishing Detection,” Springer, 2025.
8. “FedPhishLLM: Explainable and Privacy-Preserving Phishing Detection,” 2025.
9. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
10. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
11. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
12. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
13. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
14. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
15. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
16. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
17. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
18. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
19. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
20. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
21. “Heterogeneous Federated Learning for Email Security,” 2026.
22. “Federated XGBoost for Spam Detection (FL-XGB),” 2024.
