Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156495
Title: BlockFL: blockchain-enabled decentralized federated learning and model trading
Authors: Pham, Tan Anh Khoa
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Pham, T. A. K. (2022). BlockFL: blockchain-enabled decentralized federated learning and model trading. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156495
Project: SCSE21-0198 
Abstract: Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, there is only a centralized parameter server to aggregate all the local model updates, which brings the challenges of a single point of failure and server overload, especially in large-scale training scenarios. To achieve secure, reliable, and scalable FL, we leverage a sharding technique to improve scalability of the Blockchain-based Federated Edge Learning (BFEL) framework with a main chain and multiple subchains in [Kang et al., 2020]. Specifically, to release the cross-chain transaction processing workload of the main chain, the number of working consensus nodes for the main chain can be divided into multiple clusters to process multiple cross-chain transactions in parallel. This method helps reduce the execution time for FL task training and improve transaction throughput on the main chain. This project presents a working prototype to utilize blockchain and sharding techniques, thereby scaling up decentralized FL for secure, scalable and large-scale FL task training.
URI: https://hdl.handle.net/10356/156495
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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