## 4 Data Silos and Fragmentation:
### Proposed Algorithm  
In this section, we provide the details of our proposed algorithm. The proposed federated learning algorithm for \"Blockchain-Empowered Federated Learning with Electronic Medical Records\" consists of the following steps:  
1. Initialization: * Each participating healthcare institution initializes its local model parameters. * The blockchain network is set up with the necessary smart contracts, consensus mechanisms, and transaction validation protocols. 2. Model Update Exchange: * The participating institutions securely exchange their locally trained model updates using the blockchain network. * Each institution encrypts its model update to ensure privacy preservation during the exchange. * The blockchain network records and validates the encrypted model updates, maintaining an immutable history of the updates. 3. Aggregation: * The blockchain network aggregates the encrypted model updates from the participating institutions. * Aggregation can be performed using techniques such as Federated Averaging or secure multi-party computation to ensure the confidentiality of the individual updates. * The aggregated model is computed and shared back with the participating institutions. 4. Model Integration: * Each institution integrates the aggregated model with its local model parameters. * Model integration can involve techniques like model averaging, weighted averaging, or knowledge distillation to combine the aggregated model with the local knowledge. 5. Local Model Refinement: * Each institution continues training the refined model using its local EMR data. * Model refinement can be performed using standard machine learning techniques such as gradient descent or stochastic gradient descent [4]. 6. Iterative Process: * Steps 2-5 are repeated iteratively for multiple rounds, allowing the model to learn from the distributed EMR data in a collaborative manner. Convergence criteria, such as a maximum number of iterations or a defined improvement threshold, can be used to determine the termination of the federated learning process. The proposed Federated Learning Algorithm 1 leverages the power of blockchain technology to ensure secure and transparent model update exchange while preserving the privacy of the EMR data. By incorporating encryption techniques, aggregation methods, and iterative refinement, the algorithm enables the collaborative training of machine learning models on distributed EMR datasets, thereby unlocking revolutionary medical insights. ```
1:Initialization:
2: Each participating healthcare institution initializes its local model parameters. 3: The blockchain network is set up with the necessary smart contracts, consensus mechanisms, and transaction validation protocols. 4:Model Update Exchange:
5: The participating institutions securely exchange their locally trained model updates using the blockchain network. 6: Each institution encrypts its model update to ensure privacy preservation during the exchange. 7: The blockchain network records and validates the encrypted model updates, maintaining an immutable history of the updates. 8:Aggregation:
9: The blockchain network aggregates the encrypted model updates from the participating institutions. 10: Aggregation can be performed using techniques such as Federated Averaging or secure multi-party computation to ensure the confidentiality of the individual updates. 11: The aggregated model is computed and shared back with the participating institutions. 12:Model Integration:
13: Each institution integrates the aggregated model with its local model parameters. 14: Model integration can involve techniques such as model averaging, weighted averaging, or knowledge distillation to combine the aggregated model with the local knowledge. 15:Local Model Refinement:
16: Each institution continues training the refined model using its local EMR data. 17: Model refinement can be performed using standard machine learning techniques such as gradient descent or stochastic gradient descent.