المفاهيم الأساسية
Bayesian Neural Networks are proposed to manage uncertainty in distributed learning environments for AI-enabled edge devices.
الملخص
Edge IoT devices have evolved with FPGAs and AI accelerators, enhancing computational capabilities.
Challenges include optimizing AI tasks for energy and network limitations.
Research focuses on confidence levels in learning outcomes using Bayesian neural networks.
Methods explored enable collaborative learning through distributed data processing.
DiNNO algorithm extended for distributed data processing and uncertainty estimation.
Simulation of robotic platforms for collaborative mapping discussed.
State-of-the-art research on distributed machine learning methods presented.
Federated Learning, ADMM-derived methods, Federated Distillation, Split Learning, and Multi-agent Reinforcement Learning discussed.
Bayesian Neural Networks explained for stochastic training with probability distributions.
Kullback-Leibler Divergence used to quantify dissimilarity between BNN parameters' distributions.
الإحصائيات
Bayesian Neural Networks (BNNs) employ a Bayesian approach to train stochastic neural networks.
BNNs utilize probability distributions for weights and biases instead of deterministic values.
In BNNs, Gaussian distributions are commonly used for weights and biases.
اقتباسات
"Bayesian Neural Networks employ a Bayesian approach to train stochastic neural networks."
"BNNs utilize probability distributions for weights and biases instead of deterministic values."