Privacy-Preserving Federated Learning for AI-Powered Customer Personalizationoutcomes of migrating an ATG-based e-commerce application to Google Cloud

Privacy-preserving federated learning (PPFL) has been demonstrated to be a viable solution to construct AI-powered personalized models without sacrificing, in the process, the growing concerns over data privacy. In traditional machine learning frameworks, abundant amounts of sensitive data are collected, thus posing a massive privacy security problem. Federated learning addresses this problem by allowing model training on scattered devices without data centralization. However, the integration of privacy-preserving techniques into federated learning, especially customer personalization, is a subject of active research. Even with improvements in PPFL, much is still lacking in terms of research, especially in the integration of privacy-preserving techniques like differential privacy, homomorphic encryption, and secure aggregation. These need to be adapted to the model with accuracy as well as stringent privacy constraints. There are also challenges in model fairness, avoiding data biases, and enhancing the adaptability of federated learning systems for heterogeneous customer populations. Its use in e-commerce, finance, and healthcare industries has shown the potential to offer personalized services without compromising customers' sensitive information.