… Robot target recognition is a critical and fundamental machine vision task. In this paper,
InVision, a robot target recognition approach is proposed using deep federated learning. …

… Federated learning (FL) enables distributed training of complex models over isolated data
islands from remote nodes (data sources). The local training results (… with object recognition …

… only capable clients before starting the FL training phase. • We consider mobile robots to
collect federated data in a distributed fashion and perform FL training simulation for a resource-…

… ahead is blocked or free for the robot to navigate. Owing to the … are generic deep learning
models designed to aid robots in … with other possibilities, such as object detection or semantic …

… separately, due to the limitation of computation resource in terms of each individual mobile
robot [2]. Especially, for some latency-sensitive tasks, for example, real-time object detection …

… federated learning approach, we compared it to the same distributed learning system without
federated learning. … receiving all the training examples obtained by each robot locally. We …

… is widely applied to solve various problems in autonomous driving such as 3D object detection,
visual … with mobile robots using deep learning and the robot operating system,” in Robot …

… performance of object detection task is … federated robotics [163]. Both computing, networking,
and storage resources among robots are shared with a fog server for enabling federated …

… ), Federated Learning (FL) allows AI training at … robot by Robots 2 and 3 is their first actor
model in real-time. Then, in Environments 2 and 3, robots can use reinforcement learning …

… federated learning (FL) solution for detecting distracted driver activities. The proposed solution
trains the detection … Federated object detection: Optimizing object detection model with …