用检索增强生成技术优化联邦学习中客户端的精度分配，提升效率与用户体验。
RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learning
- 结合检索增强大模型与动态用户画像，智能决策客户端精度
- 实验显示满意度、能耗降低与全局模型准确率同步提升
- 适合研究高效联邦学习与个性化资源分配的工程师
混合精度计算在人工智能中广泛应用，可在精度与效率间提供更大的权衡空间。最近提出的混合精度无线联邦学习（MP-OTA-FL）使客户端根据异构硬件能力选择合适精度，利用该权衡空间并覆盖无线聚合过程中的量化开销。进一步挖掘MP-OTA-FL潜力的关键在于优化客户端精度配置。精度选择受硬件能力、潜在贡献及用户满意度等多重因素影响，这些因素往往难以定义或量化。本文提出基于RAG的用户画像框架，融合检索增强大模型与动态客户端画像，以优化满意度与贡献度。该框架包含一个混合接口用于收集设备/用户洞察，并构建了一个存储历史量化决策与反馈的RAG数据库。实验表明，所提方法在MP-OTA-FL系统中显著提升了用户满意度、节能效果与全局模型准确率。
原文摘要 · Abstract (English)
Mixed-precision computing, a widely applied technique in AI, offers a larger trade-off space between accuracy and efficiency. The recent purposed Mixed-Precision Over-the-Air Federated Learning (MP-OTA-FL) enables clients to operate at appropriate precision levels based on their heterogeneous hardware, taking advantages of the larger trade-off space while covering the quantization overheads in the mixed-precision modulation scheme for the OTA aggregation process. A key to further exploring the potential of the MP-OTA-FL framework is the optimization of client precision levels. The choice of precision level hinges on multifaceted factors including hardware capability, potential client contribution, and user satisfaction, among which factors can be difficult to define or quantify. In this paper, we propose a RAG-based User Profiling for precision planning framework that integrates retrieval-augmented LLMs and dynamic client profiling to optimize satisfaction and contributions. This includes a hybrid interface for gathering device/user insights and an RAG database storing historical quantization decisions with feedback. Experiments show that our method boosts satisfaction, energy savings, and global model accuracy in MP-OTA-FL systems.
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