… asynchronously weight updating federated learning algorithm toward the much anticipated
AI-on-Edge IoT systems… the learning tasks to the users in a federated learning framework, and …

… on recent research relating to AI on edge, divided into three … Meanwhile, the federated
learning method is widely used in … local models asynchronously for Federated Learning. The …

… by implementing asynchronously weight updating distributed learning, that we refer to as …
, “Toward asynchronously weight updating federated learning for AI-on-edge IoT systems,” in …

… Many of these models, with pre-trained weights, are … to a central edge server: synchronous
and asynchronous … Finally, the federated learning paradigm can aid cellular networks, for …

… asynchronous approaches. To apply this approach in the … FL makes a step toward protecting
data generated on each … the weights coming from each client and updates the global …

… the online creation and update of the Reinforcement Learning (RL) … of weights is reduced
and the memory footprint is optimized. … Table II presents the taxonomy of the federated learning …

… tries aggregating local models in an asynchronous way. … in AI on edge, but it accelerates
Federated learning from the … It retrains the DNN on new data while the pruned weights stay …

… lies in the usability of AI on edge devices and fog nodes to … FL technique by introducing an
asynchronous learning strategy on the … Yonetani, “Client selection for federated learning with …

… , smart homes, and smart Internet of Things, it uses technology to achieve the interaction …
It is based on the training and learning of a large number of sample data that AI can perform …

… privacy, AI on edge would instead move towards sharing model to the … the way for the
introduction of Federated Learning [1] that … The workers update their own local model weights …