# pFedBASC: Personalized Federated Learning with Blockchain-Assisted Semi-Centralized Framework
###### Abstract  
As network technology advances, there is an increasing need for a trusted new-generation information management system. Blockchain technology provides a decentralized, transparent, and tamper-proof foundation. Meanwhile, data islands have become a significant obstacle for machine learning applications. Although federated learning (FL) ensures data privacy protection, server-side security concerns persist. Traditional methods have employed a blockchain system in FL frameworks to maintain a tamper-proof global model database. In this context, we propose a novel personalized federated learning (pFL) with blockchain-assisted semi-centralized framework, pFedBASC. This approach, tailored for the Internet of Things (IoT) scenarios, constructs a semi-centralized IoT structure and utilizes trusted network connections to support FL. We concentrate on designing the aggregation process and FL algorithm, as well as the block structure. To address data heterogeneity and communication costs, we propose a pFL method called FedHyper. In this method, each client is assigned a compact hypernetwork (HN) alongside a normal target network (TN) whose parameters are generated by the HN. Clients pull together other clients' HNs for local aggregation to personalize their TNs, reducing communication costs. Furthermore, FedHyper can be integrated with other existing algorithms, enhancing its functionality. Experimental results reveal that pFedBASC effectively tackles data heterogeneity issues while maintaining positive accuracy, communication efficiency, and robustness. blockchain; semi-centralized framework; personalized federated learning; hypernetwork; non-iid data +
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reliance on trusted third parties. Transaction transparency allows all participants to view and verify transactions, while tamper-proofing ensures the authenticity and reliability of transaction information. In practical situations, acquiring a trusted server can be challenging. Blockchain satisfies requirements for decentralized, transparent, and tamper-proof data storage, catering to the needs of a decentralized FL. Consequently, blockchain-based federated learning (BFL) has emerged as a novel approach to distributed network data computation. There are several studies in BFL frameworks, with a primary focus on client selection [3; 4], aggregation weights [5; 6], local training adjustment [5; 7], privacy preservation [8; 9; 10; 11], and model compression [12]. Additionally, these studies explore consensus mechanisms [3; 4; 13], block structure [13; 14; 15], committee selection [6; 15], and incentive mechanism design for blockchain [8; 9; 10]. The papers [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16] identify several issues: the bottom-level FL has not fully exploited the potential of the blockchain, and the top-level blockchain cannot provide comprehensive support for FL. Specifically, the existing BFL framework does not fully take advantage of all network connections in real-world Internet of Things (IoT) situations, leading to high communication costs and low training efficiency. Additionally, the lack of a logical and comprehensive block structure design does not ensure sufficient protection and maintenance of users' intellectual property. FL also faces challenges such as high communication costs and data heterogeneity. With the increase in device computation capabilities and model complexity, user devices are required to transmit a large number of parameters in each training round. Simultaneously, data heterogeneity issues stemming from user behavior, regional differences, and variations in data size between individuals and organizations affect model accuracy and even convergence. In the statistical heterogeneity situation, a single global model is not suitable for highly non-independent and identically distributed (non-iid) data. Training using model decoupling methods still involves transmitting all feature knowledge to users subjectively, without providing personalized knowledge specific to local users.