Federated Learning in Robotic and Autonomous Systems

Edge computing: Vision and challenges

Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733 · see citing works

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565 · see citing works

Federated Learning With Differential Privacy: Algorithms and Performance Analysis

Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & Vincent Poor, H. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2988575 · Cited by 2,400 · see citing works

Towards Federated Learning at Scale: System Design

Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., et al. (2019) · arXiv · doi:10.48550/arxiv.1902.01046 · Cited by 2,334 · see citing works

Adaptive Federated Learning in Resource Constrained Edge Computing Systems

Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904348 · Cited by 2,232 · see citing works

Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

Nishio, T., & Yonetani, R. (2019) · ICC 2019 - 2019 IEEE International Conference on Communications (ICC) · doi:10.1109/icc.2019.8761315 · Cited by 1,875 · see citing works

Deep Reinforcement Learning That Matters

Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., & Meger, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11694 · Cited by 1,685 · see citing works

Secure, privacy-preserving and federated machine learning in medical imaging

Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0186-1 · Cited by 1,501 · cited 1× · see citing works

A 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 · see citing works

Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey

Zhao, W., Queralta, J. P., & Westerlund, T. (2020) · 2020 IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308468 · Cited by 941 · see citing works

Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

Rajeswaran, A., Kumar, V., Gupta, A., Vezzani, G., Schulman, J., Todorov, E., & Levine, S. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.049 · Cited by 847 · see citing works

A Survey of Research on Cloud Robotics and Automation

Kehoe, B., Patil, S., Abbeel, P., & Goldberg, K. (2015) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2014.2376492 · Cited by 838 · see citing works

Local Model Poisoning Attacks to Byzantine-Robust Federated Learning

Fang, M., Cao, X., Jia, J., & Gong, N. Z. (2019) · arXiv · doi:10.48550/arxiv.1911.11815 · Cited by 828 · see citing works

Personalized Federated Learning: A Meta-Learning ApproachPreprint

Fallah, A., Mokhtari, A., & Ozdaglar, A. (2020) · arXiv.org · doi:10.48550/arxiv.2002.07948 · Cited by 751 · see citing works

Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles

Lu, Y., Huang, X., Zhang, K., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2973651 · Cited by 733 · see citing works

Federated learning meets blockchain in edge computing: Opportunities and challenges

Nguyen, D. C., Ding, M., Pham, Q.-V., Pathirana, P. N., Le, L. B., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3072611 · Cited by 661 · see citing works

Federated Machine Learning: Concept and Applications

Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · arXiv · doi:10.48550/arxiv.1902.04885 · Cited by 600 · see citing works

Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges

Pokhrel, S. R., & Choi, J. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.2990686 · Cited by 531 · see citing works

BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning

Zhang, C., Li, S., XIA, J., Wang, W., Yan, F., & Liu, Y. (2020) · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · Cited by 522 · see citing works

Split learning for health: Distributed deep learning without sharing raw patient data

Vepakomma, P., Gupta, O., Swedish, T., & Raskar, R. (2018) · arXiv · doi:10.48550/arxiv.1812.00564 · Cited by 513 · see citing works

Federated learning for 6G communications: Challenges, methods, and future directions

Liu, Y., Yuan, X., Xiong, Z., Kang, J., Wang, X., & Niyato, D. (2020) · China Communications · doi:10.23919/jcc.2020.09.009 · Cited by 464 · cited 1× · see citing works

Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT

Mills, J., Hu, J., & Min, G. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2956615 · Cited by 436 · see citing works

Stochastic Gradient Push for Distributed Deep LearningPreprint

Assran, M., Loizou, N., Ballas, N., & Rabbat, M. G. (2018) · International Conference on Machine Learning · doi:10.48550/arxiv.1811.10792 · Cited by 425 · see citing works

Petuum: A New Platform for Distributed Machine Learning on Big Data

Xing, E. P., Ho, Q., Dai, W., Kim, J. K., Wei, J., Lee, S., Zheng, X., Xie, P., Kumar, A., & Yu, Y. (2015) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2015.2472014 · Cited by 388 · see citing works

Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics

Lu, Y., Huang, X., Dai, Y., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2942179 · Cited by 387 · see citing works

Survey of Personalization Techniques for Federated Learning

Kulkarni, V., Kulkarni, M., & Pant, A. (2020) · World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4) · doi:10.1109/worlds450073.2020.9210355 · Cited by 381 · see citing works

Adaptive Personalized Federated LearningPreprint

Deng, Y., Kamani, M. M., & Mahdavi, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.13461 · Cited by 366 · see citing works

A hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in Internet of Vehicles

Chai, H., Leng, S., Chen, Y., & Zhang, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3002712 · Cited by 332 · see citing works

Communication-Efficient Federated Learning for Digital Twin Edge Networks in Industrial IoT

Lu, Y., Huang, X., Zhang, K., Maharjan, S., & Zhang, Y. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3010798 · Cited by 316 · see citing works

PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems

Zhang, J., Chen, B., Cheng, X., Binh, H. T. T., & Yu, S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3023126 · Cited by 291 · see citing works

Untitled work

(2017) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 288 · see citing works

Energy-Efficient Radio Resource Allocation for Federated Edge Learning

Zeng, Q., Du, Y., Huang, K., & Leung, K. K. (2020) · 2020 IEEE International Conference on Communications Workshops (ICC Workshops) · doi:10.1109/iccworkshops49005.2020.9145118 · Cited by 286 · see citing works

Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment

Li, H., Zhang, Q., & Zhao, D. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2927869 · Cited by 262 · see citing works

Collaborate Edge and Cloud Computing With Distributed Deep Learning for Smart City Internet of Things

Wu, H., Zhang, Z., Guan, C., Wolter, K., & Xu, M. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2996784 · Cited by 261 · see citing works

Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing

Chen, J., Li, K., Deng, Q., Li, K., & Yu, P. S. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2909473 · Cited by 254 · see citing works

Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

Liu, B., Wang, L., & Liu, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2931179 · Cited by 242 · see citing works

FLChain: A Blockchain for Auditable Federated Learning with Trust and Incentive

Bao, X., Su, C., Xiong, Y. M., Huang, W., & Hu, Y. (2019) · International Conference on Big Data Computing and Communications (BigCom) · doi:10.1109/bigcom.2019.00030 · Cited by 238 · see citing works

A Survey of Deep Reinforcement Learning in Video GamesPreprint

Shao, K., Tang, Z., Zhu, Y., Li, N., & Zhao, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.10944 · Cited by 224 · see citing works

FLchain: Federated Learning via MEC-enabled Blockchain Network

Majeed, U., & Hong, C. S. (2019) · Asia-Pacific Network Operations and Management Symposium (APNOMS) · doi:10.23919/apnoms.2019.8892848 · Cited by 217 · see citing works

Learning to Detect Malicious Clients for Robust Federated LearningPreprint

Li, S., Cheng, Y. J., Wang, W., Liu, Y., & Chen, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.00211 · Cited by 202 · see citing works

To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices

Li, L., Shi, D., Hou, R.-H., Li, H., Pan, M., & Han, Z. (2020) · IEEE Conference on Computer Communications · doi:10.1109/infocom42981.2021.9488839 · Cited by 198 · see citing works

Opportunities of Federated Learning in Connected, Cooperative, and Automated Industrial Systems

Savazzi, S., Nicoli, M., Bennis, M., Kianoush, S., & Barbieri, L. (2021) · IEEE Communications Magazine · doi:10.1109/mcom.001.2000200 · Cited by 181 · see citing works

Privacy-Preserving Asynchronous Federated Learning Mechanism for Edge Network Computing

Lu, X., Liao, Y., Lio, P., & Hui, P. (2020) · IEEE Access · doi:10.1109/access.2020.2978082 · Cited by 159 · see citing works

Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design

Mo, X., & Xu, J. (2020) · J. Commun. Inf. Networks · doi:10.23919/jcin.2021.9475121 · Cited by 159 · see citing works

Edge-AI in LoRa-based Health Monitoring: Fall Detection System with Fog Computing and LSTM Recurrent Neural Networks

Queralta, J. P., Gia, T. N., Tenhunen, H., & Westerlund, T. (2019) · International Conference on Telecommunications and Signal Processing (TSP) · doi:10.1109/tsp.2019.8768883 · Cited by 154 · see citing works

Federated Learning Based Proactive Content Caching in Edge Computing

Yu, Z., Hu, J., Min, G., Lu, H., Zhao, Z., Wang, H., & Georgalas, N. (2018) · 2018 IEEE Global Communications Conference (GLOBECOM) · doi:10.1109/glocom.2018.8647616 · Cited by 140 · see citing works

Federated Learning in Mobile Edge Networks: A Comprehensive SurveyPreprint

Lim, W. Y. B., Luong, N. C., Hoang, D. T., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11875 · Cited by 138 · see citing works

Federated Transfer Reinforcement Learning for Autonomous Driving

Liang, X., Liu, Y., Chen, T., Liu, M., & Yang, Q. (2019) · arXiv · doi:10.48550/arxiv.1910.06001 · Cited by 131 · see citing works

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 · see citing works

Federated Deep Reinforcement LearningPreprint

Zhuo, H., Feng, W.-F., Lin, Y., Xu, Q., & Yang, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.08277 · Cited by 117 · see citing works

Blockchain-Based Federated Learning for Intelligent Control in Heavy Haul Railway

Hua, G., Zhu, L., Wu, J., Shen, C., Zhou, L., & Lin, Q. (2020) · IEEE Access · doi:10.1109/access.2020.3021253 · Cited by 115 · see citing works

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 · see citing works

Blockchain-Supported Federated Learning for Trustworthy Vehicular Networks

Otoum, S., Al Ridhawi, I., & Mouftah, H. T. (2020) · GLOBECOM 2020 - 2020 IEEE Global Communications Conference · doi:10.1109/globecom42002.2020.9322159 · Cited by 92 · see citing works

A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering

Tanwani, A. K., Mor, N., Kubiatowicz, J., Gonzalez, J. E., & Goldberg, K. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8793690 · Cited by 87 · see citing works

Cloud Robotics and Automation: A Survey of Related Work

Goldberg, K., & Kehoe, B. (2013) · UC Berkeley · Cited by 84 · see citing works

Federated Imitation Learning: A Novel Framework for Cloud Robotic Systems With Heterogeneous Sensor Data

Liu, B., Wang, L., Liu, M., & Xu, C. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2976321 · Cited by 83 · see citing works

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 · see citing works

Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach

Nguyen, H. T., Cong Luong, N., Zhao, J., Yuen, C., & Niyato, D. (2020) · 2020 IEEE 6th World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot48130.2020.9221089 · Cited by 75 · see citing works

A Survey on Deep Learning Methods for Robot VisionPreprint

Ruíz-del-Solar, J., Loncomilla, P., & Soto, N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10862 · Cited by 67 · see citing works

Cloud robotics in Smart Manufacturing Environments: Challenges and countermeasures

Yan, H., Hua, Q., Wang, Y., Wei, W., & Imran, M. A. (2017) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2017.05.024 · Cited by 65 · see citing works

Toward Secure and Privacy-Preserving Distributed Deep Learning in Fog-Cloud Computing

Li, Y., Li, H., Xu, G., Xiang, T., Huang, X., & Lu, R. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3012480 · Cited by 65 · see citing works

Federated learning meets blockchain at 6G edge

Pokhrel, S. R. (2020) · ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond (DroneCom) · doi:10.1145/3414045.3415949 · Cited by 65 · see citing works

A Study of Robotic Cooperation in Cloud Robotics: Architecture and Challenges

Chen, W., Yaguchi, Y., Naruse, K., Watanobe, Y., Nakamura, K., & Ogawa, J. (2018) · IEEE Access · doi:10.1109/access.2018.2852295 · Cited by 64 · see citing works

Accounting for data variability in multi-institutional distributed deep learning for medical imaging

Balachandar, N., Chang, K., Kalpathy–Cramer, J., & Rubin, D. L. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa017 · Cited by 61 · see citing works

Edge Computing to Secure IoT Data Ownership and Trade with the Ethereum Blockchain

Nawaz, A., Queralta, J. P., Guan, J., Awais, M. A., Gia, T. N., Bashir, A. K., Kan, H., & Westerlund, T. (2020) · Sensors · doi:10.3390/s20143965 · Cited by 59 · see citing works

Security and Privacy Issues in Deep Learning

Bae, H., Jang, J., Jung, D., Jang, H., Ha, H., Lee, H., & Yoon, S. (2018) · arXiv · doi:10.48550/arxiv.1807.11655 · Cited by 56 · see citing works

Communication-Efficient Distributed Deep Learning: A Comprehensive SurveyPreprint

Tang, Z.-H., Shi, S.-H., Wang, W., Li, B., & Chu, X. (2020) · arXiv.org · doi:10.48550/arxiv.2003.06307 · Cited by 54 · see citing works

Privacy-Preserving distributed deep learning based on secret sharing

Duan, J., Zhou, J., & Li, Y. (2020) · Information Sciences · doi:10.1016/j.ins.2020.03.074 · Cited by 54 · see citing works

Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

Papadopoulos, G. T., Antona, M., & Stephanidis, C. (2021) · IEEE Access · doi:10.1109/access.2021.3080517 · Cited by 54 · see citing works

Dispersed Federated Learning: Vision, Taxonomy, and Future Directions

Khan, L. U., Saad, W., Han, Z., & Hong, C. S. (2021) · IEEE Wireless Communications · doi:10.1109/mwc.011.2100003 · Cited by 46 · see citing works

Federated Reinforcement Learning

Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., & Yu, H. (2020) · Synthesis lectures on artificial intelligence and machine learning · doi:10.1007/978-3-031-01585-4_9 · Cited by 44 · see citing works

Towards Scalable Distributed Training of Deep Learning on Public Cloud ClustersPreprint

Shi, S., Zhou, X., Song, S., Wang, X., Zhu, Z., Huang, X., Jiang, X., Zhou, F., Guo, Z., Xie, L., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.10458 · Cited by 43 · see citing works

Flow-FL: Data-Driven Federated Learning for Spatio-Temporal Predictions in Multi-Robot Systems

Majcherczyk, N., Srishankar, N., & Pinciroli, C. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9560791 · Cited by 41 · see citing works

Blockchain for Mobile Edge Computing: Consensus Mechanisms and Scalability

Queralta, J. P., & Westerlund, T. (2021) · Springer eBooks · doi:10.1007/978-3-030-69893-5_14 · Cited by 40 · see citing works

Edge ai and blockchain for privacy-critical and data-sensitive applications

Nawaz, A., Gia, T. N., Queralta, J. P., & Westerlund, T. (2019) · 2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU) · doi:10.23919/icmu48249.2019.9006635 · Cited by 39 · see citing works

Joint Optimization of Data Sampling and User Selection for Federated Learning in the Mobile Edge Computing Systems

Feng, C., Wang, Y., Zhao, Z., Quek, T. Q. S., & Peng, M. (2020) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops49005.2020.9145182 · Cited by 38 · see citing works

Adaptive Task Allocation for Mobile Edge Learning

Mohammad, U., & Sorour, S. (2019) · IEEE Wireless Communications and Networking Conference Workshops (WCNCW) · doi:10.1109/wcncw.2019.8902527 · Cited by 37 · see citing works

Towards closing the sim-to-real gap in collaborative multi-robot deep reinforcement learning

Zhao, W., Queralta, J. P., Qingqing, L., & Westerlund, T. (2020) · 2020 5th International Conference on Robotics and Automation Engineering (ICRAE) · doi:10.1109/icrae50850.2020.9310796 · Cited by 36 · see citing works

Blockchain-Powered Collaboration in Heterogeneous Swarms of RobotsPreprint

Queralta, J. P., & Westerlund, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.01711 · Cited by 35 · see citing works

Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)Preprint

Zhu, G., Wang, Y., & Huang, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.11494 · Cited by 33 · see citing works

Federated Reinforcement Learning

(2024) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789811292552_0013 · Cited by 33 · see citing works

Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

Liu, B., Wang, L., & Liu, M. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967908 · Cited by 30 · see citing works

Cloud robotics: a review of technologies, developments and applications

Bogue, R. W. (2017) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir-10-2016-0265 · Cited by 29 · see citing works

Distributed Deep Learning Optimized System over the Cloud and Smart Phone Devices

Jiang, H., Starkman, J., Lee, Y., Chen, H., Qian, X., & Huang, M.-C. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2941492 · Cited by 29 · see citing works

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 · see citing works

Low-Latency Federated Reinforcement Learning-Based Resource Allocation in Converged Access Networks

Ruan, L., Mondal, S., Dias, I., & Wong, E. (2020) · Optical Fiber Communication Conference · doi:10.1364/ofc.2020.w2a.28 · Cited by 16 · see citing works

Adaptive Task Allocation for Asynchronous Federated Mobile Edge LearningPreprint

Mohammad, U., & Sorour, S. (2019) · arXiv (Cornell University) · Cited by 15 · see citing works

Exploring the Use of Synthetic Gradients for Distributed Deep Learning across Cloud and Edge Resources

Chen, Y., Zhao, K., Li, B., & Zhao, M. X. (2019) · Cited by 13 · see citing works

Training Task Allocation in Federated Edge Learning: A Matching-Theoretic Approach

Kang, J., Xiong, Z., Niyato, D. T., Cao, Z., & Leshem, A. (2020) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc46108.2020.9045112 · Cited by 13 · see citing works

User Profile-Driven Large-Scale Multi-agent Learning from Demonstration in Federated Human-Robot Collaborative Environments

Papadopoulos, G. T., Leonidis, A., Antona, M., & Stephanidis, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05409-9_40 · Cited by 13 · see citing works

Privacy Preserving Deep Learning with Distributed Encoders

Zhang, Y., Salehinejad, H., Barfett, J., Colak, E., & Valaee, S. (2019) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip45357.2019.8969086 · Cited by 11 · see citing works

Deep Reinforcement Learning based Automatic Exploration for Navigation in Unknown EnvironmentPreprint

Li, H., Zhang, Q., & Zhao, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.11808 · Cited by 10 · see citing works

To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge DevicesPreprint

Li, L., Shi, D., Hou, R., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.11804 · Cited by 10 · see citing works

Ubiquitous Distributed Deep Reinforcement Learning at the Edge: Analyzing Byzantine Agents in Discrete Action Spaces

Zhao, W., Queralta, J. P., Qingqing, L., & Westerlund, T. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.10.043 · Cited by 9 · see citing works

Hyper: Distributed Cloud Processing for Large-Scale Deep Learning Tasks

Buniatyan, D. (2019) · Computer Science and Information Technologies (CSIT) · doi:10.1109/csitechnol.2019.8895139 · Cited by 6 · see citing works

Energy-Efficient Federated Edge Learning with Joint Communication and Computation DesignPreprint

Mo, X., & Xu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.00199 · Cited by 5 · see citing works

Adaptive Task Allocation for Asynchronous Federated and Parallelized Mobile Edge LearningPreprint

Mohammad, U., & Sorour, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.01656 · Cited by 4 · see citing works

Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement LearningPreprint

Zhao, W., Queralta, J. P., Li, Q., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.07875 · Cited by 2 · see citing works

Blockchain for Mobile Edge Computing: Consensus Mechanisms and ScalabilityPreprint

Queralta, J. P., & Westerlund, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07578 · Cited by 1 · see citing works

User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative environmentsPreprint

Papadopoulos, G. T., Leonidis, A., Antona, M., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.16434

Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues

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 · see citing works

Federated Learning in Edge Computing: A Systematic Survey

Abreha, H. G., Hayajneh, M., & Serhani, M. A. (2022) · Sensors · doi:10.3390/s22020450 · Cited by 366 · see citing works

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 · see citing works

Mobile robot localization: Current challenges and future prospective

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A Review of Cloud-Edge SLAM: Toward Asynchronous Collaboration and Implicit Representation Transmission

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An Innovative Hashgraph-based Federated Learning Approach for Multi Domain 5G Network Protection

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Machine Learning in Cybersecurity

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Decentralized Over-the-Air Federated Learning by Second-Order Optimization Method

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An Intrusion Detection Scheme Based on Federated Learning and Self-Attention Fusion Convolutional Neural Network for IoT

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Proximal Control of UAVs With Federated Learning for Human-Robot Collaborative Domains

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Securing Federated Learning in Robot Swarms Using Blockchain Technology

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Personalized Mental State Evaluation in Human-Robot Interaction using Federated Learning

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Towards Accurate Training Time Estimation for On-Device Heterogeneous Federated Learning

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Application of Serverless Computing in Blockchain Distributed Technology

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When Robotics Meets Distributed Learning: the Federated Learning Robotic Network Framework

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Smart Transportation Systems for Vehicle Geographical Tracking

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Distributed Regression by Two Agents from Noisy Data

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Enhancing Robotic Autonomy and Deep Reinforcement Learning Applications

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FLAME: A Federated Learning Benchmark for Robotic Manipulation

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A Federated Learning Secure Encryption Framework for Autonomous Systems

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FedRepOpt: Gradient Re-parametrized Optimizers in Federated Learning

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Reinforcement Learning Based Collision Aware AI-Driven Intelligent Robotics Navigation System

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Federated Learning-Based Data Dissemination Systems for IoVs

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Human-Robot Interaction with Everyday Robots: A Taxonomy

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Exploring Novel Perspective on Collaborative AI: Techniques, Challenges, and Limitations

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Harnessing Federated Learning for Smart City Data Management in Cloud Environments

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A Systematic Survey on Federated Learning for Sustainable Routing in Intelligent Transportation Systems: Insights into SDGs

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Federated Learning: A Comprehensive Review of Methods, Challenges, Applications and Trends

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Developing Predictive Models by Sharing Predictions – An Investigation of a Federated Learning Approach for ADMET Predictions

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Blockchain-Enabled Federated Learning for Collaborative Autonomous Vehicles

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Digital Twin and Virtual Monitoring of a 6-DOF Robotic Manipulator

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Explainable Federated Reinforcement Learning for UAV Swarm Decision-Making

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An Overview of Federated Learning at the Edge and Distributed Ledger Technologies for Robotic and Autonomous Systems.Preprint

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Federated Learning for Robotic and Autonomous Systems: A Survey on Architectures, Synergies with Distributed Ledger Technologies, and Future Directions

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Enhancing Security and Privacy in Federated Learning for Connected Autonomous Vehicles With Lightweight Blockchain and Binius ZeroKnowledge Proofs

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Empowering Ai At The Edge: Federated Learning For Autonomous Vehicles And Multirobots

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