Reinforcement Learning assisted Adaptive difficulty of Proof of Work (PoW) in Blockchain-enabled Federated Learning
| dc.contributor.author | Sethi, Prateek | en |
| dc.contributor.committeechair | Pereira da Silva, Luiz Antonio | en |
| dc.contributor.committeechair | Pereira da Silva, Aloizio | en |
| dc.contributor.committeemember | Pirttikangas, Susanna | en |
| dc.contributor.committeemember | Doan, Thinh Thanh | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2023-08-11T08:00:09Z | en |
| dc.date.available | 2023-08-11T08:00:09Z | en |
| dc.date.issued | 2023-08-10 | en |
| dc.description.abstract | This work addresses the challenge of heterogeneity in blockchain mining, particularly in the context of consortium and private blockchains. The motivation stems from ensuring fairness and efficiency in blockchain technology's Proof of Work (PoW) consensus mechanism. Existing consensus algorithms, such as PoW, PoS, and PoB, have succeeded in public blockchains but face challenges due to heterogeneous miners. This thesis highlights the significance of considering miners' computing power and resources in PoW consensus mechanisms to enhance efficiency and fairness. It explores the implications of heterogeneity in blockchain mining in various applications, such as Federated Learning (FL), which aims to train machine learning models across distributed devices collaboratively. The research objectives of this work involve developing novel RL-based techniques to address the heterogeneity problem in consortium blockchains. Two proposed RL-based approaches, RL based Miner Selection (RL-MS) and RL based Miner and Difficulty Selection (RL-MDS), focus on selecting miners and dynamically adapting the difficulty of PoW based on the computing power of the chosen miners. The contributions of this research work include the proposed RL-based techniques, modifications to the Ethereum code for dynamic adaptation of Proof of Work Difficulty (PoW-D), integration of the Commonwealth Cyber Initiative (CCI) xG testbed with an AI/ML framework, implementation of a simulator for experimentation, and evaluation of different RL algorithms. The research also includes additional contributions in Open Radio Access Network (O-RAN) and smart cities. The proposed research has significant implications for achieving fairness and efficiency in blockchain mining in consortium and private blockchains. By leveraging reinforcement learning techniques and considering the heterogeneity of miners, this work contributes to improving the consensus mechanisms and performance of blockchain-based systems. | en |
| dc.description.abstractgeneral | Technological Advancement has led to devices having powerful yet heterogeneous computational resources. Due to the heterogeneity in the compute of miner nodes in a blockchain, there is unfairness in the PoW Consensus mechanism. More powerful devices have a higher chance of mining and gaining from the mining process. Additionally, the PoW consensus introduces a delay due to the time to mine and block propagation time. This work uses Reinforcement Learning to solve the challenge of heterogeneity in a private Ethereum blockchain. It also introduces a time constraint to ensure efficient blockchain performance for time-critical applications. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:38264 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/116016 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Blockchain | en |
| dc.subject | Machine Learning | en |
| dc.subject | Federated learning | en |
| dc.subject | Reinforcement Learning | en |
| dc.subject | MEC | en |
| dc.subject | 5G | en |
| dc.title | Reinforcement Learning assisted Adaptive difficulty of Proof of Work (PoW) in Blockchain-enabled Federated Learning | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |