为多模态智能体网络设计公平令牌分配与隐私数据估值机制
QoS-Aware Token Scheduling and Private Data Valuation for Multi-Modal Agentic Networks
- 将多模态数据嵌入共享语义空间，生成差分隐私原型
- 在资源受限下实现贡献公平性提升与服务质量优化
- 适合关注隐私保护与分布式AI协作的研究者
在智能体系统中，人类生成的数据记录决定了AI服务的价值。然而，云端计算管道将处理集中于远程服务器，导致数据中心化，削弱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：去中心化数据易受偏见、噪声影响，且分布不均。为应对这一挑战，本文研究了去中心化、资源受限环境下的公平令牌分配与隐私数据估值问题。方法将多模态表示嵌入共享语义空间，并发布差分隐私（DP）原型，在保留数据效用的同时减少语义泄露。基于DP保障，设计了公平的令牌分配方案，能有效激励高质量贡献，并对数据异质性和AI资源稀缺保持鲁棒。大量仿真表明，相比标准基准，该方法在贡献公平性和服务质量上均有提升；对图像重建攻击的更强抵抗能力也表明其在多模态个人数据隐私保护方面效果更优。
原文摘要 · Abstract (English)
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.
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