# Blockchain-Based Distributed Federated Learning in Smart Grid

^{*}

## Abstract

**:**

## 1. Introduction

- Blockchain-based distributed FL for the energy domain to support the privacy-preserving prediction of prosumers’ energy demand. The global model parameters are immutably stored and shared using blockchain network overlay.
- Smart contracts to update the global model parameters considering the challenges of integrating regression-based energy prediction, such as scaling the model parameters with prosumers’ size and blockchain transactional overhead.
- Comparative evaluation of prosumers’ energy-demand prediction using multi-layer perceptron (MLP) model distribution in three cases: centralized, local edge, and blockchain FL.

## 2. Related Work

## 3. Smart Contracts for Federated Learning

## 4. Evaluation Results

## 5. Discussion

## 6. Conclusions and Future Work

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**ML energy prediction models: (

**a**) Centralized learning; (

**b**) Federated learning (FL); (

**c**) Swarm learning (blockchain-based distributed FL).

**Figure 8.**(

**a**) Training central model on 2000 epochs (after 1100 epochs the improvements are minimal); (

**b**) Centralized learning MAPE; (

**c**) Energy prediction results for a prosumer.

**Figure 10.**(

**a**) Blockchain-based distributed FL model accuracy when trained with IID data. (

**b**) Energy prediction for prosumer #3.

**Figure 11.**(

**a**) Blockchain-based distributed FL model accuracy when trained with non-IID data; (

**b**) Energy prediction result for Prosumer #3.

Issues in Smart Grid Scenarios | FL Solutions | |
---|---|---|

Data privacy preservation and security | Non-blockchain | Distributed perturbation [35], sequential learning [36], model inversion [60], data aggregation mechanism [61], poisoning attacks mitigation [37,62], Byzantine-robust FL [63], homomorphic encryption [64], collaborative authentication protocol [65] |

Blockchain enabled | Incentivization and avoidance of model poisoning [66], blockchain for data sharing and serverless computing [53], swarm learning [30] | |

Optimization of communication costs, devices, and data heterogeneity | DANE [40], FedDANE [45], Structured and sketched updates [41], FedAvg [42], iterative algorithms [47], FedProx [46] | |

Analytics and energy efficiency | AI of things [51], load forecasting [52,53,54], energy data sharing [53,56,58], prosumer profiling [57], learning consumption patterns [59] |

Decentralized Learning Steps | Smart Contract |
---|---|

Weight vector of global model | Int256 [] globalModelWeights |

Initialize the global model | function setInitialWeights (int256[] memory weights) |

Retrieve the initial weights of the global model | function getInitialWeights () public view returns (int256[] memory) |

Push the edge nodes training results | function postLocalWeights(int256[] memory weights) |

Update the global model weights using the local models | function updateGlobalModel () public view returns (int256[] memory) |

MLP Configuration | |
---|---|

Number of input neurons | 26 |

Number of output neurons | 24 |

Number of hidden layers | 1 |

Number of neurons in hidden layer | 30 |

Activation function at hidden layer | ReLu |

Activation function at output layer | Linear |

Optimizer | SGD |

Loss function | MSE |

Kernel initializer | He uniform |

Batch size | 32 |

Prosumer | Centralized Learning | Local Learning | Blockchain-Based Distributed FL | |
---|---|---|---|---|

Non-IID | IID | |||

0 | 7.58 | 7.96 | 10.80 | 8.83 |

1 | 12.03 | 9.95 | 16.22 | 11.74 |

2 | 7.15 | 7.63 | 13.97 | 7.66 |

3 | 7.43 | 6.70 | 11.08 | 8.27 |

4 | 13.37 | 21.85 | 19.69 | 17.02 |

Test Case | MAPE | |||
---|---|---|---|---|

Min | Average | Max | ||

Blockchain-based federated learning | Non-IID | 10.80 | 14.35 | 19.69 |

IID | 7.66 | 10.62 | 17.02 | |

Centralized | 7.15 | 9.51 | 13.37 | |

Local | 6.70 | 10.82 | 21.85 |

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**MDPI and ACS Style**

Antal, M.; Mihailescu, V.; Cioara, T.; Anghel, I.
Blockchain-Based Distributed Federated Learning in Smart Grid. *Mathematics* **2022**, *10*, 4499.
https://doi.org/10.3390/math10234499

**AMA Style**

Antal M, Mihailescu V, Cioara T, Anghel I.
Blockchain-Based Distributed Federated Learning in Smart Grid. *Mathematics*. 2022; 10(23):4499.
https://doi.org/10.3390/math10234499

**Chicago/Turabian Style**

Antal, Marcel, Vlad Mihailescu, Tudor Cioara, and Ionut Anghel.
2022. "Blockchain-Based Distributed Federated Learning in Smart Grid" *Mathematics* 10, no. 23: 4499.
https://doi.org/10.3390/math10234499