Recent advancements in federated learning (FL) have greatly facilitated the development of decentralized collaborative applications, particularly in the domain of Artificial Intelligence of Things (AIoT). However, a critical aspect missing from the current research landscape is the ability to enable data-driven client models with symbolic reasoning capabilities. Specifically, the inherent heterogeneity of participating client devices poses a significant challenge, as each client exhibits unique logic reasoning properties. Failing to consider these device-specific specifications can result in critical properties being missed in the client predictions, leading to suboptimal performance. In this work, we propose a new training paradigm that leverages temporal logic reasoning to address this issue. Our approach involves enhancing the training process by incorporating mechanically generated logic expressions for each FL client. Additionally, we introduce the concept of aggregation clusters and develop a partitioning algorithm to effectively group clients based on the alignment of their temporal reasoning properties. We evaluate the proposed method on two tasks: a real-world traffic volume prediction task consisting of sensory data from fifteen states and a smart city multi-task prediction utilizing synthetic data. The evaluation results exhibit clear improvements, with performance accuracy improved by up to 54% across all sequential prediction models.

翻译：近年来，联邦学习（FL）的进展极大地促进了去中心化协作应用的发展，尤其是在人工智能物联网（AIoT）领域。然而，当前研究图景中缺失的一个关键方面是赋予数据驱动客户端模型符号推理能力。具体而言，参与客户端设备固有的异质性构成了重大挑战，因为每个客户端表现出独特的逻辑推理属性。若未考虑这些设备特定的规范，可能导致客户端预测中遗漏关键属性，从而产生次优性能。在本工作中，我们提出了一种利用时序逻辑推理解决该问题的新训练范式。我们的方法通过为每个FL客户端纳入机械生成的逻辑表达式来增强训练过程。此外，我们引入了聚合簇概念，并开发了一种分区算法，基于客户端时序推理属性的一致性对其进行有效分组。我们在两项任务上评估了所提方法：一项基于来自十五个州的传感器数据的真实交通流量预测任务，以及一项利用合成数据的智慧城市多任务预测。评估结果显示出显著改进，所有序列预测模型的性能准确率提升高达54%。