The DOI matches its Crossref record; 33 of the 36 references it cites were found in a registry or an index.
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Asynchronous Methods for Deep Reinforcement Learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1602.01783 · Cited by 7,132
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Zhu, Y., Mottaghi, R., Kolve, E., Lim, J. J., Gupta, A., Fei-Fei, L., & Farhadi, A. (2017) · 2017 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2017.7989381 · Cited by 1,533
Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., & Hadsell, R. (2016) · arXiv · doi:10.48550/arxiv.1606.04671 · Cited by 1,307
Federated Learning for Mobile Keyboard Prediction
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., & Ramage, D. (2018) · arXiv · doi:10.48550/arxiv.1811.03604 · Cited by 1,148
Reinforcement Learning with Unsupervised Auxiliary TasksPreprint
Jaderberg, M., Mnih, V., Czarnecki, W. M., Schaul, T., Leibo, J. Z., Silver, D., & Kavukcuoglu, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.05397 · Cited by 1,117
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., & Chen, M. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800286 · Cited by 1,030 · cited 1×
“Different from the traditional joint learning method where multiple edges are learning at the same time, LFRLA adopts the method of first training then mergingto reduce the dependence on the quality of communication[25][26].”
MethodFederated Learning of Deep Networks using Model AveragingPreprint
McMahan, H. B., Moore, E., Ramage, D., & y Arcas, B. A. (2016) · arXiv (Cornell University) · Cited by 1,005
Optimal and Autonomous Control Using Reinforcement Learning: A Survey
Kiumarsi, B., Vamvoudakis, K. G., Modares, H., & Lewis, F. L. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2773458 · Cited by 924
End-to-End Learning of Driving Models from Large-Scale Video Datasets
Xu, H., Gao, Y., Yu, F., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.376 · Cited by 884
Research works printed but not linked: 1
- kuffner, 0, Cloud-enabled robots, Proc Int Conf Humanoid Robot
Cited by
Sorted by citations
Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503
“The authors in [163], [164] investigate a federated imitation learning scheme for cloud robotics.”
BackgroundA survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3124599 · Cited by 1,407
Federated learning on non-IID data: A survey
Zhu, H., Xu, J., Liu, S., & Jin, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.098 · Cited by 1,062
Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. (2021) · Deakin Research Online (Deakin University) · Cited by 889
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3090430 · Cited by 892
Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3030072 · Cited by 796
A Survey on Federated Learning for Resource-Constrained IoT Devices
Imteaj, A., Thakker, U., Wang, S., Li, J., & Amini, M. H. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3095077 · Cited by 774
Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications
Aledhari, M., Razzak, R., Parizi, R. M., & Saeed, F. (2020) · IEEE Access · doi:10.1109/access.2020.3013541 · Cited by 733
Federated learning review: Fundamentals, enabling technologies, and future applications
Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., & Jararweh, Y. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103061 · Cited by 556
She, C., Sun, C., Gu, Z., Li, Y., Yang, C., Poor, H. V., & Vucetic, B. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3053601 · Cited by 405
Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399
Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., & Yu, H. (2019) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00960ed2v01y201910aim043 · Cited by 409
Federated Learning for 6G: Applications, Challenges, and Opportunities
Yang, Z., Chen, M., Wong, K., Poor, H. V., & Cui, S. R. (2021) · Engineering · doi:10.1016/j.eng.2021.12.002 · Cited by 409
FedML: A Research Library and Benchmark for Federated Machine LearningPreprint
He, C., Li, S., So, J., Zeng, X., Zhang, M., Wang, H., Wang, X., Vepakomma, P., Singh, A., Qiu, H., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.13518 · Cited by 668
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges
Lei, L., Tan, Y., Zheng, K., Liu, S., Zhang, K., & Shen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988367 · Cited by 317
A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions
Aouedi, O., Vu, T.-H., Sacco, A., Nguyen, D. C., Piamrat, K., Marchetto, G., & Pham, Q. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3430368 · Cited by 345
A Gentle Introduction to Reinforcement Learning and its Application in Different Fields
Naeem, M., Rizvi, S. T. H., & Coronato, A. (2020) · IEEE Access · doi:10.1109/access.2020.3038605 · Cited by 290
Lee, S., & Choi, D. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3035451 · Cited by 249
Federated reinforcement learning: techniques, applications, and open challenges
Qi, J., Zhou, Q., Lei, L., & Zheng, K. (2021) · doi:10.20517/ir.2021.02 · Cited by 305
Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges
Zheng, Z., Zhou, Y., Sun, Y., Wang, Z., Liu, B., & Li, K. (2021) · Connection Science · doi:10.1080/09540091.2021.1936455 · Cited by 221
Self-citationFederated Recommendation Systems
Yang, L., Tan, B., Zheng, V. W., Chen, K., & Yang, Q. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_16 · Cited by 191
Artificial Intelligence Approaches for UAV Navigation: Recent Advances and Future Challenges
Rezwan, S., & Choi, W. (2022) · IEEE Access · doi:10.1109/access.2022.3157626 · Cited by 197
Ali, M., Karimipour, H., & Tariq, M. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102355 · Cited by 177
Lo, S. K., Lu, Q., Wang, C., Paik, H.-Y., & Zhu, L. (2022) · ACM Computing Surveys · doi:10.1145/3450288 · Cited by 202
Applications of Federated Learning; Taxonomy, Challenges, and Research Trends
Shaheen, M., Farooq, M. S., Umer, T., & Kim, B.-S. (2022) · Electronics · doi:10.3390/electronics11040670 · Cited by 157
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
Yao, J., Zhang, S., Yao, Y., Wang, F., Ma, J., Zhang, J., Chu, Y., Ji, L., Jia, K., Shen, T., et al. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3178211 · Cited by 161
Reinforcement learning for robot research: A comprehensive review and open issues
Zhang, T., & Mo, H. (2021) · International Journal of Advanced Robotic Systems · doi:10.1177/17298814211007305 · Cited by 173
AFRL: Adaptive federated reinforcement learning for intelligent jamming defense in FANET
Mowla, N. I., Tran, N. H., Doh, I., & Chae, K. (2020) · Journal of Communications and Networks · doi:10.1109/jcn.2020.000015 · Cited by 146
Federated Transfer Reinforcement Learning for Autonomous Driving
Liang, X., Liu, Y., Chen, T., Liu, M., & Yang, Q. (2022) · Adaptation, learning, and optimization · doi:10.1007/978-3-031-11748-0_15 · Cited by 131
Self-citationExperiments of Federated Learning for COVID-19 Chest X-ray Images
Yan, B., Wang, J., Cheng, J., Zhou, Y., Zhang, Y., Yang, Y., Liu, L., Zhao, H., Wang, C., & Liu, B. (2021) · Communications in Computer and Information Science · doi:10.1007/978-3-030-78618-2_4 · Cited by 122
Self-citationPrivacy-preserving Federated Deep Learning for Wearable IoT-based Biomedical Monitoring
Can, Y. S., & Ersoy, C. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3428152 · Cited by 123
Federated Learning for Computationally Constrained Heterogeneous Devices: A Survey
Pfeiffer, K., Rapp, M., Khalili, R., & Henkel, J. (2023) · ACM Computing Surveys · doi:10.1145/3596907 · Cited by 125
What and How: Generalized Lifelong Spectral Clustering via Dual Memory
Sun, G., Cong, Y., Dong, J., Liu, Y., Ding, Z., & Yu, H. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3058852 · Cited by 106
Resource Allocation in IoT Edge Computing via Concurrent Federated Reinforcement Learning
Zhu, T., Zhou, W., Ye, D., Cheng, Z., & Li, J. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3086910 · Cited by 109
Federated Reinforcement Learning for Fast Personalization
Nadiger, C., Kumar, A., & Abdelhak, S. (2019) · IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) · doi:10.1109/aike.2019.00031 · Cited by 94
Hierarchical Personalized Federated Learning for User Modeling
Wu, J., Liu, Q., Huang, Z., Ning, Y., Wang, H., Chen, E., Yi, J., & Zhou, B. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449926 · Cited by 93
Li, W., Chen, J., Wang, Z., Shen, Z., Ma, C., & Cui, X. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3167482 · Cited by 91
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health
Long, G., Shen, T., Tan, Y., Gerrard, L., Clarke, A., & Jiang, J. (2022) · Humanity Driven AI · doi:10.1007/978-3-030-72188-6_6 · Cited by 85
Generative Adversarial Networks: A Literature Review
Cheng, J., Yang, Y., Tang, X., Xiong, N. N., Zhang, Y., & Lei, F. (2020) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2020.12.001 · Cited by 89
Liu, B., Wang, L., Liu, M., & Xu, C. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2976321 · Cited by 83
Self-citationFederated Learning in Robotic and Autonomous Systems
Xianjia, Y., Queralta, J. P., Heikkonen, J., & Westerlund, T. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.07.041 · Cited by 79
Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges
Tang, Q., Yu, F. R., Xie, R., Boukerche, A., Huang, T., & Liu, Y. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3175453 · Cited by 81
Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles
Zhang, Z., Wang, S., Hong, Y., Zhou, L., & Hao, Q. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561612 · Cited by 76
Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices
Lim, H.-K., Kim, J.-B., Heo, J.-S., & Han, Y. (2020) · Sensors · doi:10.3390/s20051359 · Cited by 78
Sun, Y., Ochiai, H., & Esaki, H. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3133819 · Cited by 77
Federated User Modeling from Hierarchical Information
Liu, Q., Wu, J., Huang, Z., Wang, H., Ning, Y., Chen, M., Chen, E., Yi, J., & Zhou, B. (2023) · ACM Transactions on Information Systems · doi:10.1145/3560485 · Cited by 65
Papadopoulos, G. T., Antona, M., & Stephanidis, C. (2021) · IEEE Access · doi:10.1109/access.2021.3080517 · Cited by 70
Federated Reinforcement Learning for Collective Navigation of Robotic Swarms
Na, S., Rouček, T., Ulrich, J., Pikman, J., Krajník, T., Lennox, B., & Arvin, F. (2023) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2023.3239815 · Cited by 60
A Comprehensive Survey on Collaborative Data-access Enablers in the IIoT
Sun, D., Hu, J., Wu, H., Wu, J. X., Yang, J. Y., Sheng, Q. Z., & Dustdar, S. (2023) · ACM Computing Surveys · doi:10.1145/3612918 · Cited by 57
Federated Offline Reinforcement Learning with Proximal Policy Evaluation
Yue, S., Deng, Y., Wang, G. Y., Ren, J., & Zhang, Y. (2024) · Chinese Journal of Electronics · doi:10.23919/cje.2023.00.288 · Cited by 46
Horizontal Federated Learning of Takagi–Sugeno Fuzzy Rule-Based Models
Zhu, X., Wang, D., Pedrycz, W., & Li, Z. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3118733 · Cited by 44
Lee, S., Xie, L., & Choi, D. (2021) · Sensors · doi:10.3390/s21144898 · Cited by 43
Federated deep reinforcement learning-based urban traffic signal optimal control
Li, M., Pan, X., Liu, C., & Li, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-91966-1 · Cited by 50
Federated Ensemble Model-Based Reinforcement Learning in Edge Computing
Wang, J., Hu, J. W., Mills, J., Min, G., Xia, M., & Georgalas, N. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3264480 · Cited by 44
Traffic flow combination forecasting method based on improved LSTM and ARIMA
Liu, B., Tang, X., Cheng, J., & Shi, P. (2020) · International Journal of Embedded Systems · doi:10.1504/ijes.2020.105287 · Cited by 38
Self-citationEdge-Based Federated Deep Reinforcement Learning for IoT Traffic Management
Jarwan, A., & Ibnkahla, M. A. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3174469 · Cited by 37
The Need for Advanced Intelligence in NFV Management and Orchestration
Manias, D. M., & Shami, A. A. (2020) · IEEE Network · doi:10.1109/mnet.011.2000373 · Cited by 35
Industrial Federated Learning – Requirements and System Design
Hießl, T., Schall, D., Kemnitz, J., & Schulte, S. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-51999-5_4 · Cited by 31
A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of ThingsPreprint
Zhou, J., Zhang, S., Lu, Q., Dai, W. W., Chen, M., Liu, X., Pirttikangas, S., Shi, Y., Zhang, W., & Herrera‐Viedma, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.10501 · Cited by 65
FedLight: Federated Reinforcement Learning for Autonomous Multi-Intersection Traffic Signal Control
Ye, Y., Zhao, W., Wei, T., Hu, S., & Chen, M. (2021) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac18074.2021.9586175 · Cited by 30
Ensemble and continual federated learning for classification tasks
Casado, F. E., Lema, D., Iglesias, R., Regueiro, C. V., & Barro, S. (2023) · Machine Learning · doi:10.1007/s10994-023-06330-z · Cited by 33
FC-SLAM: Federated Learning Enhanced Distributed Visual-LiDAR SLAM In Cloud Robotic System
Li, Z., Wang, L., Jiang, L., & Xu, C.-Z. (2019) · 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio49542.2019.8961798 · Cited by 28
Self-citationSynchronized path planning and tracking for front and rear axles in articulated wheel loaders
Wang, Y., Liu, X., Ren, Z., Yao, Z., & Tan, X. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105538 · Cited by 27
Mahmud, S. A., Kamarulariffin, A., Ibrahim, A., & Mohideen, A. J. H. (2024) · Journal of Intelligent and Robotic Systems · doi:10.1007/s10846-024-02149-5 · Cited by 26
Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey
Li, Y., Wang, H., Xu, W., Xiao, T., Liu, H., Tu, M., Wang, Y., Yang, X., Zhang, R., Yu, S., et al. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3568279 · Cited by 36
Federated Reinforcement Learning‐Based UAV Swarm System for Aerial Remote Sensing
Lee, W. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/4327380 · Cited by 33
A Systematic Literature Review on Federated Learning: From A Model Quality Perspective
Liu, Y., Zhang, L., Ge, N., & Li, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.01973 · Cited by 45
Jatain, D., Sıngh, V., & Dahiya, N. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.05.016 · Cited by 25
Experimental Evaluation and Analysis of Federated Learning in Edge Computing Environments
Quan, P. K., Kundroo, M., & Kim, T. (2023) · IEEE Access · doi:10.1109/access.2023.3262945 · Cited by 25
Lim, H.-K., Kim, J.-B., Kim, C.-M., Hwang, G.-Y., Choi, H.-B., & Han, Y. (2020) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic48513.2020.9065233 · Cited by 23
Federated Reinforcement Learning Acceleration Method for Precise Control of Multiple Devices
Lim, H.-K., Kim, J.-B., Ullah, I., Heo, J.-S., & Han, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3083087 · Cited by 24
Sparse Personalized Federated Learning
Liu, X., Li, Y., Wang, Q., Zhang, X., Shao, Y., & Geng, Y. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3250658 · Cited by 26
Federated Meta Reinforcement Learning for Personalized Tasks
Liu, W., Xu, X., Wu, J., & Jiang, J. (2023) · Tsinghua Science & Technology · doi:10.26599/tst.2023.9010066 · Cited by 23
Selvi, G. C., Murugan, R., Yenduri, G., Gurrammagari, D. R., Bhulakshmi, D., Kandati, D. R., Supriya, Y., Gadekallu, T. R., Rathore, R. S., & Jhaveri, R. H. (2023) · Recent Advances in Computer Science and Communications · doi:10.2174/0126662558266152231128060222 · Cited by 22
Tong, Z., Deng, J., Mei, J., Zhang, Y., & Li, K. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3478841 · Cited by 22
Pokhrel, S. R. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07065-z · Cited by 22
Towards Lifelong Federated Learning in Autonomous Mobile Robots with Continuous Sim-to-Real Transfer
Yu, X., Queralta, J. P., & Westerlund, T. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.10.123 · Cited by 22
A systematic review on federated learning system: a new paradigm to machine learning
Chaudhary, R. K., Kumar, R., & Saxena, N. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02257-6 · Cited by 24
Grasso, C., Raftopoulos, R., Schembra, G., & Serrano, S. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109449 · Cited by 21
Federated dueling DQN based microgrid energy management strategy in edge-cloud computing environment
Li, H., Yang, Y., Liu, Y., & Pei, W. (2024) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2024.101329 · Cited by 21
Jing, Y., Guo, B., Li, N., Xu, R. R., & Yu, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128729 · Cited by 25
Privacy-Preserving Federated Deep Reinforcement Learning for Mobility-as-a-Service
Chu, K.-F., & Guo, W. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3317358 · Cited by 20
Kou, W.-B., Wang, S., Zhu, G., Luo, B., Chen, Y., Ng, D. W. K., & Wu, Y. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342134 · Cited by 20
Wang, T., & Dong, Z. Y. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123107 · Cited by 19
Federated Learning Application on Depression Treatment Robots(DTbot)
Liu, Y., & Yang, R. (2021) · International Conference on Computer Research and Development · doi:10.1109/iccrd51685.2021.9386709 · Cited by 19
Kabir, R., Watanobe, Y., Ding, D., Islam, M. R., & Naruse, K. (2025) · IEEE Access · doi:10.1109/access.2025.3619776 · Cited by 17
Swarm Deep Reinforcement Learning for Robotic Manipulation
Zhu, X., Zhang, F., & Li, H. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2021.12.272 · Cited by 17
Hudson, N., Oza, P., Khamfroush, H., & Chantem, T. (2022) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp55677.2022.00021 · Cited by 17
Gradient Monitored Reinforcement Learning for Jamming Attack Detection in FANETs
Ghelani, J., Gharia, P., & El‐Ocla, H. (2024) · IEEE Access · doi:10.1109/access.2024.3361945 · Cited by 18
Federated Learning for Collaborative Robotics: A ROS 2-Based Approach
Gutierrez-Quintana, G., Rincon, J. A., & Julián, V. (2025) · Electronics · doi:10.3390/electronics14071323 · Cited by 21
Federated Meta-Learning for Traffic Steering in O-RAN
Erdöl, H., Wang, X., Li, P., Thomas, J. D., Piechocki, R. J., Oikonomou, G., Inacio, R., Ahmad, A., Briggs, K., & Kapoor, S. (2022) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2022-fall57202.2022.10012789 · Cited by 16
Karami, M., & Karami, A. (2025) · Cluster Computing · doi:10.1007/s10586-025-05250-y · Cited by 24
Multi-Task Federated Reinforcement Learning with AdversariesPreprint
Anwar, A., & Raychowdhury, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.06473 · Cited by 17
Deep Learning for Embodied Vision Navigation: A SurveyPreprint
Zhu, F., Zhu, Y., Lee, V. C., Liang, X., & Chang, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.04097 · Cited by 30
Mishra, S., Punitha, S., Harikrishna, T., Swamy, K. N., & Loganayagi, S. (2023) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss58487.2023.10250582 · Cited by 14
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps
Jiang, X., Hu, H., On, T., Lai, P., Mayyuri, V. D., Chen, A., Shila, D. M., Larmuseau, A., Jin, R., Borcea, C., et al. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3223578 · Cited by 15
Chen, W., Chen, S., Leng, J.-W., Wang, J.-K., Guan, Y.-S., Meng, M. Q.-H., & Zhang, H. (2024) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2024.3438165 · Cited by 20
Rjoub, G., Elmekki, H., Bentahar, J., Pedrycz, W., Kassaymeh, S., Almobydeen, S. B., & Rachida Dssouli, A. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113574 · Cited by 14
Abulaish, M., Wasi, N. A., & Sharma, S. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1526 · Cited by 13
Metadata
- 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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
