# Federated Learning-Based Resource Management with Blockchain Trust Assurance in Smart IoT
###### Abstract
A client-edge-cloud hierarchical FL framework was proposed in [17]. A collaborative FL (CFL) mechanism was proposed in [6], which enables the edge devices to implement FL when they cannot communicate with the FL server for some reasons. Although FL is distributed, its central controller can pose security issues. Blockchain was applied to improve the security and non-repudiation of the FL mechanism in [18; 19; 20; 21; 22; 23]. IEEE published the guide \"an Architectural Framework for Blockchain-based Federated Machine Learning\" in late 2021, which defined the types of blockchain-based federated machine learning, as well as application scenarios and other aspects from a high-level perspective [18]. In [19], malicious nodes were identified before participating in FL training. In [20], a Blockchain Federated Learning architecture (BlockFL) was proposed, and the end-to-end latency model of BlockFL was analyzed. In [21], a fine-grained FL mechanism with a blockchain-based reputation guarantee was proposed to achieve trustworthy FL training in edge networks. In [22], a blockchain-assisted decentralized FL framework was proposed to solve the single point failure problem of FL servers. Although privacy and resource allocation issues were further investigated in [22], no specific resource allocation algorithm was given. In the blockchain-based FL framework proposed in [23], the FL server incentivized the participating nodes based on the accuracy of their uploaded model parameters to improve the accuracy of the classification problem. As the further development of joint FL and blockchain, a new concept, Swarm Learning (SL), was proposed in [24]. Unlike FL, SL is a new distributed ML technique with no central controller. In [24], SL was used to detect patients with severe diseases quickly and reliably. However, SL nodes are expectedto be independent and homogeneous, i.e., they should preferably have similar capabilities. Therefore, SL may not be particularly suitable for heterogeneous IoT end devices. We first analyze the key challenges of resource management in the future smart IoT, which motivates the research of this paper. Considering the heterogeneity of IoT end-device capabilities, we use FL for distributed intelligent management of IoT resources. In this approach, the central FL server aggregates the local model parameters from the end devices with different capabilities; then, a reliable global model can be quickly obtained and be shared among all FL nodes. To ensure the reliability of FL model parameters exchange, the FL nodes are deployed in the blockchain. In addition, we optimize FL training participant selection by performing detection on malicious nodes. The main contribution of this paper is an in-depth study of the FL-based resource management mechanism with blockchain trust assurance, and a specific resource management algorithm is given. The rest is organized as follows: Section 2 investigates the challenges of resource management in future smart IoT. Section 3 analyzes the federated learning-based resource management mechanism with blockchain trust assurance. Section 4 proposes a specific resource management algorithm. Section 5 performs the simulation analysis. Section 6 concludes this paper.