μDFL: A Secure Microchained Decentralized Federated Learning Fabric Atop IoT Networks

Abstract

Federated Learning (FL) has been recognized as a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on the aggregation of distributed local model updates. However, security and privacy guarantees could be compromised due to malicious participants and the centralized aggregation manner. Possessing attractive features like decentralization, immutability and auditability, Blockchain is promising to enable a tamper-proof and trust-free framework to enhance performance and security in IoT based FL systems. However, directly integrating blockchains into the large scale IoT-based FL scenarios still faces many limitations, such as high computation and storage demands, low transactions throughput, poor scalability and challenges in privacy preservation. This paper proposes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> DFL, a novel hierarchical IoT network fabric for decentralized federated learning (DFL) atop of a lightweight blockchain called microchain. Following the hierarchical infrastructure of FL, participants in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> DFL are fragmented into multiple small scale microchains. Each microchain network relies on a hybrid Proof of Credit (PoC) block generation and Voting-based Chain Finality (VCF) consensus protocol to ensure efficiency and privacy-preservation at the network of edge. Meanwhile, microchains are federated vie a high-level inter-chain network, which adopts an efficient Byzantine Fault Tolerance (BFT) consensus protocol to achieve scalability and security. A proof-of-concept prototype is implemented, and the experimental results verify the feasibility of the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> DFL solution in cross-devices FL settings with efficiency, security and privacy guarantees.