In agentic systems, human-generated data records anchor the value of AI services. Yet cloud compute pipelines centralize processing on remote servers. Data centralization reduces personal data sovereignty and may potentially degrade the quality of service (QoS). Meanwhile, user contributions are diverse in quantity and quality: decentralized records can be biased, noisy, and heterogeneously distributed. To address the data challenge, we study fair token allocation and private data valuation for decentralized and resource-constrained agentic systems. Our approach embeds multi-modal representations in a shared semantic space and releases differentially private (DP) prototypes to preserve utility while reducing semantic leakage. With the DP guarantee, we design a fair token allocation scheme that rewards effective contributions and remains robust to data heterogeneity and AI resource scarcity. Extensive simulations demonstrate improved contribution-based fairness and QoS compared to standard benchmarks. The improved resistance to image reconstruction attacks indicates enhanced privacy for multi-modal personal data.

翻译：在智能体系统中，人类生成的数据记录锚定了人工智能服务的价值。然而，云计算管道将处理过程集中到远程服务器上。数据集中化降低了个人数据主权，并可能降低服务质量（QoS）。同时，用户贡献在数量和质量上存在差异：去中心化的记录可能存在偏差、噪声和异质性分布。为了应对数据挑战，我们研究了去中心化且资源受限的智能体系统中的公平令牌分配和隐私数据估值问题。我们的方法将多模态表示嵌入到共享语义空间中，并发布满足差分隐私（DP）的原型，以在减少语义泄露的同时保留实用性。在DP保证下，我们设计了一种公平的令牌分配方案，该方案奖励有效贡献，并能够应对数据异质性和AI资源稀缺性。大量仿真实验表明，与标准基线相比，所提方法在基于贡献的公平性和QoS方面均有提升。对图像重建攻击的更强抵抗力表明多模态个人数据的隐私性得到了增强。