Federated Learning Infrastructure for Privacy-Preserving Personalized Shopping in E-Commerce
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I4P108Keywords:
Federated Learning, Low-Latency AI, Personalized Recommendations, E-commerce, Privacy-Preserving Machine Learning, Real-Time Inference, LoLaFLAbstract
The modern e-commerce landscape is defined by a fundamental tension between the strategic imperative for real-time, personalized user experiences and the dual constraints of stringent privacy regulations and high-latency computation. While personalized recommendations are critical for navigating information overload and driving engagement, traditional centralized architectures pose significant data privacy risks. Federated Learning (FL) has emerged as a foundational, privacy-preserving paradigm by training models on decentralized user data. However, standard FL implementations suffer from a severe communication bottleneck, introducing latency that is prohibitive for time-sensitive applications like product recommendations at checkout. This paper addresses this performance gap by introducing a new class of tooling exemplified by the Low-Latency Federated Learning (LoLaFL) framework. It details the architectural shift from conventional backpropagation-based FL to a novel forward-only, white-box framework. This approach dramatically reduces communication overhead and computational complexity, enabling the low-latency inference required to deliver secure, private, and instantaneous personalized recommendations at the most critical point of the customer journey
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