FairFedAds: A Fairness-Aware Federated Advertising Framework with Differential Privacy and Incentive Alignment for Social Commerce Ecosystems
Keywords:
federated learning, differential privacy, fairness, advertising, social commerce, incentive alignment, attribution, privacy-preserving systemsAbstract
Social commerce ecosystems have transformed digital advertising by blending social interaction with commercial transactions, yet they introduce profound challenges related to data privacy, fairness, and participant incentive alignment. Conventional centralized advertising platforms collect vast amounts of user data, raising privacy concerns and creating systemic biases that disproportionately affect underrepresented groups. Federated learning offers a promising alternative by enabling collaborative model training without raw data centralization, but its application to advertising must address the unique dynamics of social commerce, including creator monetization, multi-stakeholder fairness, and cross-device heterogeneity. This paper presents FairFedAds, a fairness-aware federated advertising framework that integrates differential privacy, incentive alignment mechanisms, and equitable attribution modeling. The framework is designed to preserve individual privacy through adaptive differential privacy budgets, ensure fairness across advertisers, creators, and users via group-fairness constraints and demographic parity objectives, and sustain participation through game-theoretic incentive structures. We discuss the architectural trade-offs among privacy, accuracy, fairness, and computational efficiency, and examine deployment considerations such as governance, regulatory compliance, and cross-platform interoperability. Through conceptual analysis and comparison with existing approaches, we argue that FairFedAds provides a viable blueprint for privacy-preserving, equitable, and economically sustainable advertising in social commerce ecosystems. The paper concludes with future research directions that address dynamic fairness auditing, long-term incentive stability, and integration with emerging decentralized identity frameworks.
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