In the Social Internet of Things (SIoT), where users interact in a distributed manner, attackers exploit system vulnerabilities to manipulate trust. These attackers spread false information and services, build deceptive reputations, and gain user trust to achieve malicious goals. Such strategies, known as trust-related attacks, involve falsified ratings or manipulated feedback to artificially boost the reputation of malicious entities within the network. To counter these attacks, trust management systems play an essential role in identifying and mitigating malicious activity. Blockchain technology has revolutionized decentralized and distributed systems, providing enhanced security through various applications. Although integrating blockchain into trust management poses challenges, it significantly improves trust evaluation and strengthens the overall security framework. This work introduces a blockchain-based secure trust management system that addresses vulnerabilities through an authentication layer powered by zero-knowledge proof technology, ensuring privacy and robust validation. In addition, a trust evaluation model based on federated learning is proposed, designed to manage heterogeneous data from diverse SIoT nodes with constrained computational resources. The proposed approach is designed to detect various types of trust-related attacks, fostering trustworthy interactions within SIoT environments.