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+## Federated Averaging
+
+#### Some General Remarks
+
+The general architecture of Exareme 2 follows a Master/Worker paradigm where many Workers
+, operating in multiple locations, are coordinated by one Master. Only Workers
+are allowed access to the anonymized data in each location and the Master only
+sees aggregate data, derived from the full data and sent to him by the Workers.
+
+Our naming convention is that procedures run on Workers are given the adjective _local_
+whereas those running on Master are called _global_.
+
+In this premise, we implement federated averaging by building the models locally, using 
+state-of-the-art Python libraries, such as scikit learn and then averaging the parameters
+on the global node.
+
+#### Algorithm Description
+
+This algorithm aggregates the parameters of the local models and returns their average.
+
+<b><h4>Algorithm Implementation</b></h4>
+
+[FedAvg](../../exareme2/algorithms/in_database/fedaverage.py)