  Countries across the globe have been pushing strict regulations on the
protection of personal or private data collected. The traditional centralized
machine learning method, where data is collected from end-users or IoT devices,
so that it can discover insights behind real-world data, may not be feasible
for many data-driven industry applications in light of such regulations. A new
machine learning method, coined by Google as Federated Learning (FL) enables
multiple participants to train a machine learning model collectively without
directly exchanging data. However, recent studies have shown that there is
still a possibility to exploit the shared models to extract personal or
confidential data. In this paper, we propose to adopt Multi Party Computation
(MPC) to achieve privacy-preserving model aggregation for FL. The MPC-enabled
model aggregation in a peer-to-peer manner incurs high communication overhead
with low scalability. To address this problem, the authors proposed to develop
a two-phase mechanism by 1) electing a small committee and 2) providing
MPC-enabled model aggregation service to a larger number of participants
through the committee. The MPC enabled FL framework has been integrated in an
IoT platform for smart manufacturing. It enables a set of companies to train
high quality models collectively by leveraging their complementary data-sets on
their own premises, without compromising privacy, model accuracy vis-a-vis
traditional machine learning methods and execution efficiency in terms of
communication cost and execution time.
