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Recent advancements in federated learning (FL) have greatly facilitated thedevelopment of decentralized collaborative applications, particularly in thedomain of Artificial Intelligence of Things (AIoT). However, a critical aspectmissing from the current research landscape is the ability to enabledata-driven client models with symbolic reasoning capabilities. Specifically,the inherent heterogeneity of participating client devices poses a significantchallenge, as each client exhibits unique logic reasoning properties. Failingto consider these device-specific specifications can result in criticalproperties being missed in the client predictions, leading to suboptimalperformance. In this work, we propose a new training paradigm that leveragestemporal logic reasoning to address this issue. Our approach involves enhancingthe training process by incorporating mechanically generated logic expressionsfor each FL client. Additionally, we introduce the concept of aggregationclusters and develop a partitioning algorithm to effectively group clientsbased on the alignment of their temporal reasoning properties. We evaluate theproposed method on two tasks: a real-world traffic volume prediction taskconsisting of sensory data from fifteen states and a smart city multi-taskprediction utilizing synthetic data. The evaluation results exhibit clearimprovements, with performance accuracy improved by up to 54% across allsequential prediction models.

Formal Logic Enabled Personalized Federated Learning Through Property Inference