Next-Gen AI Architectures for Telecom: Federated Learning, Graph Neural Networks, and Privacy-First Customer Automation

Abstract

The telecom commerce industry generates volumes of customer interaction data across diverse channels-voice, text, and digital platforms-on an unprecedented scale. Traditional modes of processing data are insufficient to deal with the complexity and real-time needs of this high-dimensional and unstructured data. The present paper reviews advanced AI and machine learning models being applied for improving customer interactions and automation in the domain of telecom commerce. The present work is meant to discuss deep learning architecture applications, namely CNN and RNN, in processing textual and speech data with the intent of sentiment analysis and intent recognition. Reinforcement learning algorithms are adopted in optimizing customer engagement strategies, by learning policies that maximize customer satisfaction and increased revenue generation. GNNs have also been used to model complex relationships among customers for personalized recommendations and targeting marketing efforts. Actual deployment of such models requires robust system architectures, using API-driven platforms with microservices for handling scalability, modularity, and interoperability. Optimization techniques, like model quantization and pruning, leverage the computationally efficient nature for their deployment on resource-constrained platforms such as edge devices. With this respect, different techniques such as differential privacy and federated learning are discussed that can preserve the security concerns without compromising on model performance. It clearly appears that integrations of these advanced models and algorithms within API-driven systems may further improve customer interaction and automation capabilities in telecom commerce.