Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
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This letter was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the letter, we propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website to provide the service based on LFRL: www.shared-robotics.com.
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The imitation learning efficiency and accuracy of local robots can be improved in comparison with centralized learning approaches.
“Through communication rounds, the server can accumulate a significant knowledge of different robots to build a powerful learning model, and thus the imitation learning efficiency and accuracy of local robots can be improved in comparison with centralized learning approaches.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [164]
The Evaluation for the architecture section (page unavailable) reports that the shared model increased navigation-decision accuracy and reduced training time in Env-2 through Env-4. But the study evaluates reinforcement learning against generic methods trained from scratch, not imitation learning against centralized learning approaches. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThrough communication rounds, the server can accumulate significant knowledge of different robots to build a powerful learning model.
“Through communication rounds, the server can accumulate a significant knowledge of different robots to build a powerful learning model, and thus the imitation learning efficiency and accuracy of local robots can be improved in comparison with centralized learning approaches.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [164]
FL can assist to improve the security and privacy of medical industry data analysis and prediction by incorporating a data protection system.
“FL can assist to improve the security and privacy of medical industry data analysis and prediction by incorporating a data protection system [25, 161, 162] .”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [161]
Hospital mortality prediction, duration of stay, or rather the rate of admission can also be examined using FL-based models for the proper management of some critical care units in the hospital while protecting the privacy of in-hospital data.
“Hospital mortality prediction, duration of stay, or rather the rate of admission can also be examined using FL-based models for the proper management of some critical care units in the hospital while protecting the privacy of in-hospital data [161, 166].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [161]
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- Authors
- Liu, B., Wang, L., & Liu, M.
- Affiliations
- Shenzhen Institutes of Advanced Technology (CN)
- Hong Kong University of Science and Technology (HK)
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2019-10
- Volume
- 4
- Issue
- 4
- Pages
- 4555-4562
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2911450448
- Cited by
- 242
- References
- 36
- Citations by year
- 2026: 5 · 2025: 29 · 2024: 38 · 2023: 40 · 2022: 33 · 2021: 50 · 2020: 27 · 2019: 20
- FWCI
- 15.00
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Robotics and Automation Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Shenzhen Science and Technology Innovation Commission (JCYJ2017081853518789)
- Guangdong Science and Technology Plan Guangdong-Hong Kong Cooperation Innovation Platform (2018B050502009)
- National Natural Science Foundation of China (61603376, U1713211)
- Shenzhen Science, Technology and Innovation Commission (JCYJ20160428154842603)
- Basic Research Project of Shanghai Science and Technology Commission (16JC1401200)
- Chengzhong Xu from University of Macau
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Engineering › Control and Systems Engineering › Robotics and Automated Systems
- Keywords
- lifelong learning, federated reinforcement learning, robot navigation, cloud robotics, knowledge fusion, transfer learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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