Traditional Machine Learning (ML) methods usually rely on a central server to per-form ML tasks. However, these methods have problems like security risks, datastorage issues, and high computational demands. Federated Learning (FL), on theother hand, spreads out the ML process. It trains models on local devices and thencombines them centrally. While FL improves computing and customization, it stillfaces the same challenges as centralized ML in security and data storage.
This thesis introduces a new approach combining Federated Learning and Decen-tralized Machine Learning (DML), which operates on an Ethereum Virtual Machine(EVM) compatible blockchain. The blockchain’s security and decentralized naturehelp improve transparency, trust, scalability, and efficiency.
The main contributionsof this thesis include:1. Redesigning a semi-centralized system with enhanced privacy and the multi-KRUM algorithm, following the work of Shayan et al..2. Developing a new decentralized framework that supports both standard anddeep-learning FL, using the InterPlanetary File System (IPFS) and EthereumVirtual Machine (EVM)-compatible Smart Contracts.3. Assessing how well the system defends against common data poisoning attacks,using a version of Multi-KRUM that’s better at detecting outliers.4. Applying privacy methods to securely combine data from different sources.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-4407 |
| Date | 01 December 2023 |
| Creators | Sridhar, Nikhil |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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