Fed-AdAgent: A High-Throughput Distributed Infrastructure for Social Commerce via Privacy-Preserving LLMs and Incentive-Compatible Mechanism Design - Hollingsworth, Theodore; Ashford, Charles (Computational Intelligence Systems, 2026)
The convergence of social media and electronic commerce has birthed a complex socio-technical ecosystem known as social commerce, where interpersonal interactions and commercial transactions are inextricably linked. However, the expansion of this sector is increasingly hindered by the inherent tension between personalized recommendation accuracy and the imperative for user data privacy. This paper introduces Fed-AdAgent, a novel high-throughput distributed infrastructure designed to harmonize these competing interests through the deployment of privacy-preserving Large Language Models (LLMs) and incentive-compatible mechanism design. Fed-AdAgent utilizes a federated learning paradigm to localize the fine-tuning of LLM-based agents on edge devices, ensuring that sensitive behavioral data remains within the user's personal sphere. To address the computational bottlenecks associated with large-scale distributed inference, the system implements a hierarchical orchestration layer that optimizes throughput via adaptive model sharding and asynchronous gradient aggregation. Beyond the technical architecture, we provide a rigorous analysis of the system's economic foundations, proposing a mechanism design that incentivizes honest participation from both users and advertisers while mitigating the risks of algorithmic collusion. The discussion extends to broader system-level trade-offs, including the energy sustainability of decentralized AI, the robustness of the infrastructure against adversarial poisoning, and the policy implications for global data governance. By synthesizing advances in engineering, game theory, and ethics, this research provides a comprehensive blueprint for the next generation of social commerce platforms that are both high-performing and privacy-centric.