Blockchain-Enabled Federated Learning for Realtime Energy Prediction Using Stackelberg-Shapley-Based Privacy-Conscious Coalition Strategies

Abstract

This research tackles the challenges of non-independent and identically distributed (non-IID) data, socio-political inequalities in decentralized energy networks, and the unpredictable nature of renewable energy sources. It integrates blockchain technology with federated learning (FL) and game theory. Cluster-based FL paired with Shapley value allocation helps mitigate data heterogeneity, while a combined Stackelberg-Shapley model implemented via smart contracts facilitates adaptive pricing strategies. To safeguard user privacy, the system incorporates zero-knowledge proofs, differential privacy$(\varepsilon=0.5)$, and CKKS-based homomorphic encryption, achieving a 98% resistance rate against cyberattacks. Field tests in the EU's NER400 sandbox and blockchain-enabled microgrids in Kenya confirm the framework's effectiveness—achieving a 4.2% mean absolute percentage error (MAPE) in forecasting (improving from a 12% benchmark), curbing renewable energy certificate (REC) fraud by 89%, and cutting rural energy expenses by 40%. Leveraging a hybrid consensus model (PBFT with Sharding), the platform supports over 10,000 per second with sub-second latency, bridging interoperability gaps between Ethereum-based REC systems and Hyperledger platforms.