Decentralised and collaborative machine learning framework for IoT
Martín González-Soto Rebeca P. Díaz-Redondo Manuel Fernández-Veiga Bruno Rodríguez-Castro Ana Fernández-Vilas
Decentralised machine learning has recently been proposed as a potential
solution to the security issues of the canonical federated learning approach.
In this paper, we propose a decentralised and collaborative machine learning
framework specially oriented to resource-constrained devices, usual in IoT
deployments. With this aim we propose the following construction blocks. First,
an incremental learning algorithm based on prototypes that was specifically
implemented to work in low-performance computing elements. Second, two
random-based protocols to exchange the local models among the computing
elements in the network. Finally, two algorithmics approaches for prediction
and prototype creation. This proposal was compared to a typical centralized
incremental learning approach in terms of accuracy, training time and
robustness with very promising results.