Date of Award


Degree Name

MS in Computer Science


Computer Science


College of Engineering


Franz J. Kurfess

Advisor Department

Computer Science

Advisor College

College of Engineering


Traditional Machine Learning (ML) methods usually rely on a central server to per-
form ML tasks. However, these methods have problems like security risks, data
storage issues, and high computational demands. Federated Learning (FL), on the
other hand, spreads out the ML process. It trains models on local devices and then
combines them centrally. While FL improves computing and customization, it still
faces 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 nature
help improve transparency, trust, scalability, and efficiency.

The main contributions
of 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 and
deep-learning FL, using the InterPlanetary File System (IPFS) and Ethereum
Virtual 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.