Multi-dimensional Data Quick Query for Blockchain-based Federated Learning

TL;DR

This paper introduces MerkleRB-Tree, a novel data structure that significantly improves multi-dimensional metadata query efficiency in blockchain-based federated learning, enabling better participant selection.

Contribution

The paper proposes MerkleRB-Tree, combining MBRs and bloom filters, along with a skip list approach, to enhance multi-dimensional query efficiency within and across blockchain blocks.

Findings

Outperforms existing methods in query speed on benchmark datasets

Effectively handles multi-dimensional continuous and discrete attributes

Enhances participant selection reliability in blockchain-based FL

Abstract

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.