mlsanityData to 7 Oct 2026

Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data

arXiv 1909.00895 ↗First posted 3 Sep 2019cs.RO cs.AI cs.LGcited by 4

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning

Abstract (arXiv)

Humans are capable of learning a new behavior by observing others perform the skill. Robots can also implement this by imitation learning. Furthermore, if with external guidance, humans will master the new behavior more efficiently. So how can robots implement this? To address the issue, we present Federated Imitation Learning (FIL) in the paper. Firstly, a knowledge fusion algorithm deployed on the cloud for fusing knowledge from local robots is presented. Then, effective transfer learning methods in FIL are introduced. With FIL, a robot is capable of utilizing knowledge from other robots to increase its imitation learning. FIL considers information privacy and data heterogeneity when robots share knowledge. It is suitable to be deployed in cloud robotic systems. Finally, we conduct experiments of a simplified self-driving task for robots (cars). The experimental results demonstrate that FIL is capable of increasing imitation learning of local robots in cloud robotic systems.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 10 of its 28 references that are in the map. First posted on arXiv 3 Sep 2019.

Builds on

10 of 28 references.

References · 10

  1. 2016End to End Learning for Self-Driving Carscited by 1,000
  2. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  3. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  4. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 286
  5. 2018One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learningcited by 146
  6. 2019Recent Advances in Imitation Learning from Observationcited by 92
  7. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  8. 2019Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systemscited by 77
  9. 2018Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learningcited by 75
  10. 2019Network Offloading Policies for Cloud Robotics: a Learning-based Approachcited by 17

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 0; 2023: 2.

4 papers.

  1. 2021Deep Federated Learning for Autonomous Drivingcited by 54
  2. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  3. 2021A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Thingscited by 11
  4. 2023Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devicescited by 7

Similar, not cited

Close in wording, no citation either way

  1. 2019Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems with Heterogeneous Sensor Data
  2. 2019Efficient Supervision for Robot Learning via Imitation, Simulation, and Adaptation
  3. 2018An Algorithmic Perspective on Imitation Learning
  4. 2022On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios
  5. 2023IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisors
  6. 2020Scalable Multi-Task Imitation Learning with Autonomous Improvement
  7. 2025Data-Free Continual Learning of Server Models in Model-Heterogeneous Cloud-Device Collaboration
  8. 2022Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer