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 | Schools: | School of Computer Science and Engineering | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Student Reports (FYP/IA/PA/PI) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FYP Final report.pdf Restricted Access | FYP Final Report | 1.74 MB | Adobe PDF | View/Open |
Page view(s)
277
Updated on Mar 26, 2024
Download(s) 50
24
Updated on Mar 26, 2024
Google ScholarTM
Check
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.