我的研究提出了名為 FLNet 的系統架構，這是一個基於 Hyperledger Fabric 聯盟區塊鏈 (Corsortium Blockchain) 並且結合了聯邦學習 (Federated Learning) 的區塊鏈網路，其目的是為了讓組織或企業可以在系統上與其客戶或其他利益相關者能在不洩漏本地數據的情況下，於區塊鏈網路上共同訓練深度學習模型，以保持高度的安全及隱私。近年來區塊鏈與人工智慧成為兩個最熱門的技術，也開始有人提出了 Decentralized AI 的想法，其概念就是結合 AI 與區塊鏈技術。因為中心化數據庫的資料可能會被竄改，且資料的出處與真實性無法被保證，倘若數據遭惡意人士操縱就會導致訓練出來的 AI 之可信度下降，甚至做出錯誤的決策。Hyperledger Fabric 聯盟區塊鏈的出現很好地解決了此議題，它不僅保有了原區塊鏈的可追溯性與不可竄改性，並且還可以使用基於 Raft Protocol 的共識演算法來增進事務處理效率。此外，Fabric 中的節點之間也互相知曉彼此身分，且還能透過通道機制來維持組織間與商業事務相關之數據的隱私性和機密性。如今 AI 已被廣泛使用，人們發現其最大的阻礙是在於如何獲取大量數據，其中以高度敏感的隱私資料最難取得，因此我演示了一個聯邦學習演算法運行於聯盟區塊鏈中的流程，讓組織與使用者可以在區塊鏈中共同訓練深度學習模型並且毌須上傳本地敏感數據至區塊鏈帳本，使得所有數據保有最高的安全性及隱私性。

關鍵字 – 聯盟區塊鏈、Hyperledger Fabric、去中心化人工智慧、聯盟式學習

This work proposed a system architecture called FLNet, a blockchain network based on the Hyperledger Fabric and Federated Learning. The purpose is to allow organizations to jointly train deep learning models with their customers or other stakeholders on a consortium blockchain network without leaking local data to keep the highest security and privacy. Currently, blockchain and AI have become popular technologies. People begin to put forward decentralized AI, which is to combine blockchain and AI. Data in a centralized database may be tampered with, and the provenance and authenticity of data cannot be guaranteed. Once the above situations happen, the credibility of AI models will reduce, and even wrong decisions will be made. Fabric solved this issue well. It maintains the traceability and immutable property of blockchain and can use Raft protocol-based consensus algorithm to improve transaction processing efficiency. Users are not willing to provide private information for training models based on the privacy rule. Hence, we demonstrate a flow that runs federated learning on Hyperledger Fabric. In this way, organizations and customers can jointly train deep learning models without uploading sensitive local data to obtain better security and privacy.

Keywords - Consortium blockchain, Hyperledger Fabric, Decentralized AI, Federated learning

摘要 i
Abstract ii
誌謝 　 iii
Acknowledgments iv
Contents v
List of Figures viii
List of Tables x
Abbreviations xi
1 Introduction 1
1.1 Motivation .................................... 1
1.2 Purpose ....................................... 3
1.3 Thesis Architecture ........................... 4
2 Background and Related Works 5
2.1 Blockchain .................................... 5
2.1.1 Types of Blockchain ......................... 6
2.1.2 Smart Contracts ............................. 8
2.1.3 Consensus Algorithm ......................... 9
2.2 Hyperledger Fabric ............................ 10
2.2.1 Chaincode ................................... 11
2.2.2 Channel Mechanism ........................... 13
2.2.3 Pluggable Consensus ......................... 13
2.3 Artificial Intelligence ....................... 15
2.4 Decentralized AI .............................. 16
2.5 Federated Learning ............................ 17
3 Problem Formulation 21
3.1 Federated Learning on Public Blockchain ....... 21
3.2 Proposed FLNet System ......................... 24
4 Simulation Environment 26
4.1 Server PC ..................................... 26
4.2 Hyperledger Fabric ............................ 27
4.3 Docker and Docker Compose ..................... 28
5 System Architecture 30
5.1 System Overview ............................... 30
5.2 Hyperledger Fabric Network Operation .......... 33
5.2.1 Hyperledger Fabric Components ............... 33
5.2.2 Hyperledger Fabric Transaction Flow ......... 42
5.3 Combine Federated Learning Algorithm .......... 49
5.3.1 Make Data Contribution Proposals ............ 50
5.3.2 Train and Commit Local Models ............... 52
5.3.3 Aggregate into a Global Model ............... 52
6 Experimental Results 58
6.1 Dataset ....................................... 58
6.2 FLNet Example Network ......................... 59
6.3 Experimental Results .......................... 63
7 Conclusion 67
7.1 Discussion .................................... 67
7.2 Future Work ................................... 68
Bibliography 69

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