Securing Social Commerce Infrastructure through Privacy Preserving Federated Learning Synergizing Multi-Modal Large Language Models and Differential Privacy
Keywords:
Social Commerce Infrastructure, Federated Learning, Multi-Modal Large Language Models, Differential Privacy, Privacy-Preserving Computation, Socio-Technical Governance, Distributed SystemsAbstract
The convergence of social media and electronic commerce has birthed a complex socio-technical ecosystem known as social commerce, which relies heavily on the ingestion of heterogeneous user data to drive personalized recommendation engines. However, the centralization of multi-modal data—including text, images, and transactional behaviors—poses significant privacy risks and systemic vulnerabilities. This paper proposes a comprehensive architectural framework for securing social commerce infrastructure by integrating privacy-preserving federated learning with multi-modal large language models and differential privacy mechanisms. By shifting the paradigm from centralized data aggregation to decentralized model training, the proposed system ensures that sensitive user attributes remain on-device while allowing the global model to benefit from collective intelligence. We provide a deep analytical exploration of the system-level trade-offs between privacy guarantees, computational overhead, and recommendation accuracy. The discussion emphasizes the structural requirements for hardware-aware orchestration and the necessity of robust governance frameworks to manage autonomous agentic behaviors in digital marketplaces. Furthermore, we examine the socio-technical implications of this infrastructure, focusing on algorithmic fairness, environmental sustainability, and the evolving global policy landscape regarding data sovereignty. By synergizing the semantic depth of multi-modal transformers with the mathematical rigor of differential privacy, this research offers a resilient blueprint for the next generation of social commerce, ensuring that commercial efficiency does not come at the expense of individual privacy or systemic security.
References
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308-318.
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and creates labor. Journal of Economic Perspectives, 33(2), 3-30.
Agrawal, R., & Srikant, R. (2000). Privacy-preserving data mining. ACM Sigmod Record, 29(2), 439-450.
Bonawitz, K., et al. (2017). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 1175-1191.
Bommasani, R., et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
Brown, T., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
Cho, J. H., et al. (2020). Toward a sustainable and resilient social commerce infrastructure: A systems perspective. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(12), 4987-5002.
Dwork, C. (2008). Differential privacy: A survey of results. International Conference on Theory and Applications of Models of Computation, 1-19.
Gentry, C. (2009). Fully homomorphic encryption using ideal lattices. Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing, 169-178.
Hard, A., et al. (2018). Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778.
Kairouz, P., et al. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1-2), 1-210.
Kaplan, J., et al. (2020). Scaling laws for neural language models. arXiv preprint arXiv:2001.08361.
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016). Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492.
Liu, T. (2026). A Comparative Study of Transformer-Based and Classical Models for Financial Time-Series Forecasting. Journal of Risk and Financial Management, 19(3), 203.
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50-60.
McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273-1282.
Mohri, M., Sivek, G., & Suresh, A. T. (2019). Agnostic federated learning. International Conference on Machine Learning, 4615-4625.
Narayanan, A., & Shmatikov, V. (2008). Robust de-anonymization of large sparse datasets. 2008 IEEE Symposium on Security and Privacy, 111-125.
O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.
Radford, A., et al. (2021). Learning transferable visual models from natural language supervision. International Conference on Machine Learning, 8748-8763.
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017). Membership inference attacks against machine learning models. 2017 IEEE Symposium on Security and Privacy (SP), 3-18.
Stoica, I., et al. (2017). Ray: A distributed framework for emerging AI applications. 13th USENIX Symposium on Operating Systems Design and Implementation, 561-577.
Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Wu, S., et al. (2023). BloombergGPT: A large language model for finance. arXiv preprint arXiv:2303.17564.
Yi, X. (2026). A Federated and Differentially Private Incentive–Marketing Framework for Privacy-Preserving Cross-Channel Measurement in AI-Powered Digital Commerce.
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19.
Zaharia, M., et al. (2012). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. 9th USENIX Symposium on Networked Systems Design and Implementation, 15-28.
Zhang, C., et al. (2021). Survey on differential privacy with machine learning. IEEE Access, 9, 13329-13350.
Zhao, Y., et al. (2018). Federated learning with non-iid data. arXiv preprint arXiv:1806.00582.
Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.
Mo, F., Haddadi, H., Katiyar, K., Ansari, R., & Chuah, C. N. (2021). PPFL: Privacy-preserving federated learning with trusted execution environments. Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, 94-108.
Wang, J., et al. (2021). A field guide to federated optimization. arXiv preprint arXiv:2107.06917.
Rothchild, D., et al. (2020). FetchSGD: Communication-efficient federated learning with sketching. Proceedings of the 37th International Conference on Machine Learning.
Kairouz, P., et al. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1-2), 1-210.
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