# A Survey on Blockchain-Based Federated Learning
## 1 Introduction
[15] demonstrated the feasibility of reconstructing input data from gradient information, independent of the deep network architecture, and recovered a batch of input images using cosine similarity and adversarial attack methods. Secondly, the attainment of a global model in FL involves multiple iterative rounds of model updates from users, engendering significant communication overhead and incurring additional storage costs during network transmission [16; 17]. Moreover, the distinction between federated learning and distributed computing lies in the fact that the dataset in FL comes from various end-user terminals, and the features of data generated by these users are often non-independent and non-identically distributed (non-IID). Traditional distributed framework algorithms perform well only when dealing with independent and identically distributed (IID) data, while they encounter challenges such as difficulty in convergence and excessive communication rounds when handling non-IID data [18]. Thirdly, the integrity of the global model may be compromised due to malevolent participants or a central server susceptible to cyber-attacks [19]. Lastly, the local devices involved could themselves be malicious or vulnerable to exploitation, potentially resulting in the unauthorized disclosure or manipulation of transmitted information [20]. In recent years, blockchain technology, originating from Bitcoin, has undergone rapid advancement [21]. Built upon a decentralized peer-to-peer network architecture, blockchains ensure that transactional data are stored across all network nodes, while its immutability and consistency are guaranteed by consensus algorithms. Innovatively establishing decentralized trust, blockchain technology allows individuals to opt for believing in the reliability of cryptographic algorithms and the honesty of the majority of nodes within the peer-to-peer network, rather than being compelled to place trust in a single entity [22]. This mechanism of decentralized trust offers a new avenue for augmenting the capabilities of FL. For instance, FL can not only leverage the consistency provided by blockchain's consensus mechanisms to establish trustworthy interactions within an untrusted environment but can also utilize the economic property derived from blockchain's incentive schemes to effectively promote information sharing within the federated ecosystem. Through the accumulated technical advancements in FL and blockchains over the years, as well as their exploration and applications in various relevant fields, blockchain-based federated learning (BFL) has gained the capability and prospects for applications in highly privacy-sensitive industries. Due to the advantages of blockchain in areas such as identity verification, decentralization, traceability, and immutability, many research efforts have used blockchains as underlying structures for FL. They achieve distributed model aggregation tasks by designing protocols on top of the blockchain. While blockchain is an effective way to replace the central server in FL and enhances security in the storage and update processes of FL models, it also introduces new challenges in FL application scenarios, such as training efficiency, resource allocation, and communication delays. At present, limited literature exists that explores the integration of blockchain and FL. [PERSON] et al. [23] introduced the categories and platforms of blockchain technologies employed in existing BFL research work, and made comparisons between various BFL frameworks. [PERSON] et al. [24] compared and summarized some prevailing BFL frameworks, underlying BFL infrastructures, and applications of BFL. [PERSON] et al. [25] engaged in a comprehensive survey targeting FL, wherein the comparative analysis spanned aspects such as architectural paradigms, communication efficiency, incentive mechanisms, privacy preservation, and secure aggregation schemes, and also incorporated an investigation of certain BFL architectures. [PERSON] et al.