Decentralised Reputation Tracking using TinyML for Task Recommendation in Spatial Crowdsourcing

Spatial crowdsourcing (SC) platforms depend on reputation systems to ensure trust and quality in the execution of tasks. However, traditional centralised reputation mechanisms face scalability, privacy, and connectivity challenges. This paper presents a decentralised reputation tracking framework that utilises Tiny Machine Learning (TinyML). In this framework, lightweight machine learning models embedded in edge devices compute and maintain local reputation scores based on the outcomes of tasks. These local scores are then shared with a central task allocator, allowing for trust-aware assignments without exposing raw behavioural data. The proposed modular architecture facilitates on-device feature extraction and reputation updates, significantly reducing communication overhead. Experimental results using Yelp datasets indicate that our approach achieves competitive trust estimation accuracy, decreases communication by $45 \%$, and a $4 x$ reduction in latency compared to centralised baselines. This work establishes a foundation for privacy-preserving, scalable, and intelligent trust management in the next generation of SC systems.