  The increasing requirements for data protection and privacy has attracted a
huge research interest on distributed artificial intelligence and specifically
on federated learning, an emerging machine learning approach that allows the
construction of a model between several participants who hold their own private
data. In the initial proposal of federated learning the architecture was
centralised and the aggregation was done with federated averaging, meaning that
a central server will orchestrate the federation using the most straightforward
averaging strategy. This research is focused on testing different federated
strategies in a peer-to-peer environment. The authors propose various
aggregation strategies for federated learning, including weighted averaging
aggregation, using different factors and strategies based on participant
contribution. The strategies are tested with varying data sizes to identify the
most robust ones. This research tests the strategies with several biomedical
datasets and the results of the experiments show that the accuracy-based
weighted average outperforms the classical federated averaging method.
