When Swarm Learning meets energy series data: A decentralized collaborative learning design based on blockchain

TL;DR

This paper introduces a blockchain-based decentralized collaborative learning framework called Swarm Learning for energy data, enhancing privacy, security, and model stability compared to traditional centralized and federated approaches.

Contribution

It proposes a novel Swarm Learning scheme utilizing blockchain and smart contracts for secure, transparent, and decentralized energy data modeling, addressing privacy and policy constraints.

Findings

Outperforms local learning methods in energy series forecasting.

Enhances data security and privacy over centralized and federated learning.

Model stability improves with increased data volume and local epochs.

Abstract

Machine learning models offer the capability to forecast future energy production or consumption and infer essential unknown variables from existing data. However, legal and policy constraints within specific energy sectors render the data sensitive, presenting technical hurdles in utilizing data from diverse sources. Therefore, we propose adopting a Swarm Learning (SL) scheme, which replaces the centralized server with a blockchain-based distributed network to address the security and privacy issues inherent in Federated Learning (FL)'s centralized architecture. Within this distributed Collaborative Learning framework, each participating organization governs nodes for inter-organizational communication. Devices from various organizations utilize smart contracts for parameter uploading and retrieval. Consensus mechanism ensures distributed consistency throughout the learning process, guarantees the transparent trustworthiness and immutability of parameters on-chain. The efficacy of the proposed framework is substantiated across three real-world energy series modeling scenarios with superior performance compared to Local Learning approaches, simultaneously emphasizing enhanced data security and privacy over Centralized Learning and FL method. Notably, as the number of data volume and the count of local epochs increases within a threshold, there is an improvement in model performance accompanied by a reduction in the variance of performance errors. Consequently, this leads to an increased stability and reliability in the outcomes produced by the model.