Due to the drawbacks of Federated Learning (FL) such as vulnerability of a single central server, centralized federated learning is shifting to decentralized federated learning, a paradigm which takes the advantages of blockchain. A key enabler for adoption of blockchain-based federated learning is how to select suitable participants to train models collaboratively. Selecting participants by storing and querying the metadata of data owners on blockchain could ensure the reliability of selected data owners, which is helpful to obtain high-quality models in FL. However, querying multi-dimensional metadata on blockchain needs to traverse every transaction in each block, making the query time-consuming. An efficient query method for multi-dimensional metadata in the blockchain for selecting participants in FL is absent and challenging. In this paper, we propose a novel data structure to improve the query efficiency within each block named MerkleRB-Tree. In detail, we leverage Minimal Bounding Rectangle(MBR) and bloom-filters for the query process of multi-dimensional continuous-valued attributes and discrete-valued attributes respectively. Furthermore, we migrate the idea of the skip list along with an MBR and a bloom filter at the head of each block to enhance the query efficiency for inter-blocks. The performance analysis and extensive evaluation results on the benchmark dataset demonstrate the superiority of our method in blockchain-based FL.

翻译：由于联邦学习（FL）存在单一中心服务器脆弱性等缺陷，集中式联邦学习正转向利用区块链优势的去中心化联邦学习范式。采用基于区块链的联邦学习的关键在于如何选择合适的参与者协同训练模型。通过将数据所有者的元数据存储于区块链并进行查询来选择参与者，可确保所选数据所有者的可靠性，有助于在联邦学习中获取高质量模型。然而，在区块链上查询多维元数据需遍历每个区块中的所有交易，导致查询耗时。目前尚缺乏一种在联邦学习中选择参与者时高效查询区块链多维元数据的有效方法，这仍是一个具有挑战性的问题。本文提出一种名为MerkleRB-Tree的新型数据结构，用于提升区块内查询效率。具体而言，我们利用最小边界矩形（MBR）和布隆过滤器分别对多维连续值属性和离散值属性进行查询处理。此外，我们借鉴跳表思想，在每个区块头部集成MBR与布隆过滤器，以提升跨区块查询效率。基于基准数据集的性能分析与充分评估结果表明，我们的方法在基于区块链的联邦学习中具有显著优越性。