Uncertainty Estimation in Multi-Agent Distributed Learning for
  AI-Enabled Edge Devices
Gleb Radchenko Victoria Andrea Fill
Initially considered as low-power units with limited autonomous processing,
Edge IoT devices have seen a paradigm shift with the introduction of FPGAs and
AI accelerators. This advancement has vastly amplified their computational
capabilities, emphasizing the practicality of edge AI. Such progress introduces
new challenges of optimizing AI tasks for the limitations of energy and network
resources typical in Edge computing environments. Our study explores methods
that enable distributed data processing through AI-enabled edge devices,
enhancing collaborative learning capabilities. A key focus of our research is
the challenge of determining confidence levels in learning outcomes,
considering the spatial and temporal variability of data sets encountered by
independent agents. To address this issue, we investigate the application of
Bayesian neural networks, proposing a novel approach to manage uncertainty in
distributed learning environments.