Swarm Learning for temporal and spatial series data in energy systems: A decentralized collaborative learning design based on blockchain

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 scheme, which replaces the centralized server with a blockchain-based distributed network to address the security and privacy issues inherent in Federated Learning'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. The 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 two real-world temporal and spatial series data modeling scenarios in energy systems: photovoltaic power generation forecasting and geophysical well log generation. Our approach shows superior performance compared to Local Learning methods while emphasizing enhanced data security and privacy over both Centralized Learning and Federated Learning methods.