  Ensemble learning combines results from multiple machine learning models in
order to provide a better and optimised predictive model with reduced bias,
variance and improved predictions. However, in federated learning it is not
feasible to apply centralised ensemble learning directly due to privacy
concerns. Hence, a mechanism is required to combine results of local models to
produce a global model. Most distributed consensus algorithms, such as
Byzantine fault tolerance (BFT), do not normally perform well in such
applications. This is because, in such methods predictions of some of the peers
are disregarded, so a majority of peers can win without even considering other
peers' decisions. Additionally, the confidence score of the result of each peer
is not normally taken into account, although it is an important feature to
consider for ensemble learning. Moreover, the problem of a tie event is often
left un-addressed by methods such as BFT. To fill these research gaps, we
propose PoSw (Proof of Swarm), a novel distributed consensus algorithm for
ensemble learning in a federated setting, which was inspired by particle swarm
based algorithms for solving optimisation problems. The proposed algorithm is
theoretically proved to always converge in a relatively small number of steps
and has mechanisms to resolve tie events while trying to achieve sub-optimum
solutions. We experimentally validated the performance of the proposed
algorithm using ECG classification as an example application in healthcare,
showing that the ensemble learning model outperformed all local models and even
the FL-based global model. To the best of our knowledge, the proposed algorithm
is the first attempt to make consensus over the output results of distributed
models trained using federated learning.
