Causality-Aware Federated Reinforcement Learning for Privacy-Preserving Budget Allocation in Social Commerce Advertising Ecosystems
Keywords:
federated reinforcement learning, causal inference, budget allocation, social commerce, differential privacy, advertising ecosystems, privacy-preserving machine learning, fairness, governance, cross-device attributionAbstract
The rapid expansion of social commerce advertising ecosystems has created a critical need for budget allocation mechanisms that are both operationally efficient and privacy compliant. Traditional centralized approaches to budget optimization rely on aggregating granular user behavior data, raising significant privacy concerns and violating emerging regulatory frameworks. Meanwhile, conventional federated reinforcement learning methods, while addressing data decentralization, often fail to account for the structural causal relationships that drive advertising effectiveness in complex socio-technical systems. This paper proposes a causality-aware federated reinforcement learning framework designed specifically for privacy-preserving budget allocation across cross-device social commerce platforms. The architecture integrates three foundational components: a federated infrastructure that ensures raw user data never leaves local devices, a causal inference module that models the counterfactual impact of ad exposures on downstream conversions, and a multi-agent reinforcement learning layer that dynamically allocates budgets across creators, channels, and time periods under differential privacy constraints. We examine the structural trade-offs between statistical utility, privacy guarantees, and fairness across heterogeneous advertiser segments. The governance implications of such systems are analyzed, including issues of algorithmic transparency, accountability for budget decisions, and the prevention of discriminatory outcomes. Deployment considerations are discussed with respect to computational sustainability, communication overhead, and robustness to adversarial threats. We further situate the proposed framework within the broader policy landscape, including the implications of data minimization principles and zero-trust architectures. By synthesizing insights from causal inference, federated learning, and reinforcement learning, this work provides a comprehensive blueprint for next-generation advertising infrastructure that aligns economic incentives with ethical and legal mandates. The paper concludes with a forward-looking discussion on the integration of large language model-assisted policy generation and the potential for cultural bias in multi-regional deployments.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
