Client Flow

FLock Client Loop

1. Claim Outstanding Rewards:

Automatically retrieve and claim any uncollected rewards for previous epochs, enhancing user incentive alignment.

2. Role Assignment Wait Period:

Pause the workflow and wait for the assignment of a specific role to the user within the federated learning process.

3. Role-Based Actions:

The client executes different actions based on the assigned role:

i. Proposer:

1. Download global model parameters:

   Retrieve the latest global model parameters from the blockchain.

2. Retrain model with dataset:

   Use the input dataset to fine-tune the global model parameters locally.

3. Upload updated model parameters:

   Store the updated parameters on IPFS for permissionless.

4. Submit contribution hash:

   Register the contribution with the FlockManager to provide a transparent record of contributions.

ii. Voter:

1. Download global model & calculate initial accuracy:

   Retrieve latest parameters and assess initial accuracy on the user's test dataset partition.

2. Retrieve current epoch's contributions:

   Gather the contribution hashes for the current learning epoch.

3. Download contributions from IPFS:

   Retrieve the localised insights for aggregation.

4. Aggregate contributions via averaging:

   Compile individual contributions to update the global model.

5. Upload aggregated model to IPFS:

   Store the updated model securely and transparently.

6. Push aggregated model hash to blockchain:

   Register the aggregated model's hash for validation.

7. Calculate aggregated model accuracy:

   Test the updated model on the user's data partition to assess performance.

8. Determine output accuracy:

   Compute the final accuracy metric.

9. Submit vote score:

   Cast a vote based on the accuracy delta (step 8 vs step 1), contributing to the consensus process.

10. Training Completion Check:

    Continue the loop, executing the appropriate steps for the assigned role, until the federated learning process reaches its defined completion criteria.

The loop ensures a seamless and continuous engagement with the FLock ecosystem, allowing participants to contribute to the learning process and aligning their incentives