# Federated Learning-Based Resource Management with Blockchain Trust Assurance in Smart IoT
###### Abstract  
Resource management is a key issue that needs to be addressed in the future smart Internet of Things (IoT). This paper focuses on a Federated Learning (FL)-based resource management mechanism in IoT. It incorporates blockchain technology to guarantee the security of the FL model parameters exchange. We propose an IoT resource management framework incorporating blockchain and federated learning technologies; then, a specific FL-based resource management with a blockchain trust assurance algorithm is given. We use a Support Vector Machine (SVM) classifier to detect malicious nodes in order to avoid the impact on the performance of the FL-based algorithm. Finally, we perform simulation to verify the SVM classification effect and the proposed algorithm performance. The results show that the SVM-based malicious node identification accuracy can be acceptable. Moreover, the proposed algorithm obtains better performance when malicious nodes are excluded from the FL selected participant. Internet of Things (IoT); federated learning (FL); blockchain; resource management; malicious nodes +
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0000-0001-5882-2274]K.K. the traditional centralized mechanism, in FL, the local model computation is performed at the node that generates the data and only the updated model parameters are uploaded. These local model parameters are aggregated in the FL server for generating the global model [7; 8]. However, all IoT nodes involved in FL training should be trustworthy. Otherwise, it will seriously affect the reliability and validity of the aggregated global model. IoT nodes may be deployed in harsh or remote areas. Coupled with the fact that the IoT topology often changes dynamically, the network often has difficulty with identifying the inclusion of malicious nodes [9]. According to the \"2020 IoT Threat Report\" published by PaloAltoNetworks' security team Unit 42, 57% of IoT devices are vulnerable to medium- or high-severity attacks [10]. Therefore, it is crucial to identify the malicious nodes to avoid the bad effects on FL. Blockchain is a distributed database technology for storing transaction records. It uses some kind of consensus algorithm to reach a consensus among different nodes. It also enables non-repudiation among blocks so that none of the nodes can deny their actions [11]. IoT nodes can be deployed in blockchains. A safe and reliable service guarantee for the model parameters exchange can then be provided. In addition, IoT companies are exploring the applications of \"IoT + blockchains\". In 2020, Cat.1 blockchain module-L610 was first launched in China, which is the world's leading developed based on the IoT chips platform and can be loaded into IoT devices. Future IoT devices that integrate the next-generation IoT modules and blockchain modules will have more powerful capabilities [12]. In recent years, using FL for wireless network performance optimization and using blockchain to improve privacy protection in IoT have become two research hotspots. Refs. [4; 13; 14] conducted a comprehensive survey on the application of FL techniques in wireless networks and analyzed various issues, challenges, and research directions. However, they did not investigate specific FL algorithms. In [15], FL was used to solve the joint power and radio resource allocation problem for ultra-reliable and low-latency communication in vehicular networks. However, the reliability of FL training and transmission was not considered in [15]. The authors in [16] studied specific scheduling strategies and analyzed the impact of transmission errors on FL performance, but they did not perform FL participant selection to optimize the performance of the resource allocation algorithm. In most FL studies, the cloud servers in the core network are used as FL servers, while the FL local nodes are at the edge of the network. With the advent of mobile edge computing and the enhancement of terminal capabilities, FL migrates to the network edge and terminals [6; 13; 17].