###### Abstract  
Federated learning (FL) is emerging as a powerful paradigm for distributed data mining in the context of Internet of Things (IoT) big data. It addresses privacy concerns associated with data outsourcing by enabling local data training and knowledge (i.e., model) sharing. However, simplistic local knowledge sharing can inadvertently expose user privacy to advanced attacks, such as model inversion or gradient leakage. Furthermore, achieving fine-grained and personalized privacy protection for IoT users remains a challenge. In this paper, we propose a novel solution called hierarchical blockchain-empowered cloud-edge orchestrated federated learning (HBCE-FL) to address these challenges. HBCE-FL is designed to provide secure, intelligent, and distributed data analysis for IoT users. To tackle FL's privacy issues, we develop a multi-level access control encryption and blockchain-based approach for sharing IoT knowledge within the HBCE-FL framework. Our approach classifies IoT users into different levels based on their individual privacy requirements, enabling fine-grained privacy protection. The blockchain is employed for identity authentication, key management, and message sanitization. For scenarios involving IoT users with non-IID data, we integrate federated multi-task learning into HBCE-FL to ensure fairness, robustness, and privacy. Finally, we conduct experiments using classic MNIST and CIFAR10 datasets to validate our approach. The experimental results illustrate that HBCE-FL effectively achieves personalized privacy-preserving FL without losing IoT data availability. Regardless of whether IoT data are homogeneous or heterogeneous, our approach enhances model accuracy and convergence rates by enabling secure IoT knowledge access and sharing for IoT users. federated learning; privacy protection; blockchain; knowledge sharing; Internet of Things 2024  
(SMC) [12], homomorphic encryption [13], and differential privacy [14]. SMC and homomorphic encryption techniques all have a high overhead and some complex operations are difficult to handle. Differential privacy, which safeguards data through noise introduction, impacts data availability and leads to a decrease in model accuracy. The cost of different privacy protection mechanisms is reduced accuracy or efficiency [15], and they introduce an inevitable conflict between protecting privacy and achieving higher training performance for clients [16]. Moreover, in distributed collaborative training, it is difficult to guarantee that participants are trustworthy. Untrusted clients participating in training with malicious or erroneous data may compromise the accuracy of the global model and even lead to more serious consequences. Blockchain is a distributed tamper-proof, encrypted linked data block, and some studies introduce blockchain technology into a FL framework to store access control strategies [17] and leave traces of interactive information [18]. However, the transparency of blockchain storage still poses the risk of data leakage. Diverse device attributes and variations in the importance of local IoT data have introduced a multitude of privacy-preserving requirements among FL clients. High-privacy clients, in particular, are inclined to safeguard their data and knowledge from untrusted servers. Yet, isolating clients based on their privacy needs--separating clients with high privacy requirements from their general counterparts for individual model training--often carries the risk of diminishing model performance due to reduced data sample availability. Our observation leads us to consider information flow control as a viable privacy protection mechanism. In our approach, we categorize both clients and servers according to their security and privacy requirements, devising access control regulations that dictate the FL process between different security tiers. High-security level clients can collaborate by sharing data for training, with data aggregation taking place on high-security level servers.