> For the complete documentation index, see [llms.txt](https://docs.flock.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.flock.io/flock-products/fl-alliance/task-lifecycle-deep-dive/3.-fl-aggregation-and-evaluation.md).

# 3. FL Aggregation and Evaluation

Then, each voter gathers the local models from all proposers. These models are then aggregated into the latest global model using a weighted averaging approach. Each voter then proceeds to evaluate the aggregated model, utilising their own local testing datasets. This evaluation phase involves the computation of a local validation score, which functions as a criterion for assessing the model’s performance. These individual validation scores

are then submitted to a smart contract for aggregation. Following the aggregation, the aggregated score is compared with the previous round’s score, to assess progress or decline in model performance. The smart contract then determines the next steps for the aggregated model based on these scores: if there is an improvement in performance score, the system will proceed to the next phase of training; if there is a decline, another round of training, aggregation and evaluation will begin using the validated model from the previous round. 

After receiving all reported voting results from the voters, the aggregator (any participant can call the function through the smart contract) will calculate the aggregated voting result.

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