Fetching the paper…
Reading the bibliography…
Federated learning has been explored as a promising solution for training at the edge, where end devices collaborate to train models without sharing data with other entities.
Femtocaching: Wireless content delivery through distributed caching helpers
Karthikeyan Shanmugam, Negin Golrezaei, Alexandros G. Dimakis, Andreas F. Molisch, and Giuseppe Caire · 2013
Earlier work this paper cites.
Towards resource sharing in mobile device clouds: Power balancing across mobile devices
Abderrahmen Mtibaa, Afnan Fahim, Khaled A Harras, and Mostafa H Ammar · 2013
Earlier work this paper cites.
Live migration of virtualized edge networks: Analytical modeling and performance evaluation
Franco Callegati and Walter Cerroni · 2013
Earlier work this paper cites.
Live migration of virtual machines among edge networks viawan links
Donatella Darsena, Giacinto Gelli, Antonio Manzalini, Fulvio Melito, and Francesco Verde · 2013
Earlier work this paper cites.
Cloud services, networking, and management
Nelson LS da Fonseca and Raouf Boutaba · 2015
Earlier work this paper cites.
Developing iot applications in the fog: A distributed dataflow approach
N. K. Giang, M. Blackstock, R. Lea, and V. C. M. Leung · 2015
Earlier work this paper cites.
Data center energy consumption modeling: A survey
Miyuru Dayarathna, Yonggang Wen, and Rui Fan · 2015
Earlier work this paper cites.
Towards virtual machine migration in fog computing
L. F. Bittencourt, M. M. Lopes, I. Petri, and O. F. Rana · 2015
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Earlier work this paper cites.
A federated edge cloud-iot architecture
D. Kelaidonis, A. Rouskas, V. Stavroulaki, P. Demestichas, and P. Vlacheas · 2016
Earlier work this paper cites.
Revisiting distributed synchronous sgd
Jianmin Chen, Xinghao Pan, Rajat Monga, Samy Bengio, and Rafal Jozefowicz · 2016
Earlier work this paper cites.
Focusstack: Orchestrating edge clouds using location-based focus of attention
Brian Amento, Bharath Balasubramanian, Robert J Hall, Kaustubh Joshi, Gueyoung Jung, and K Hal Purdy · 2016
Earlier work this paper cites.
Paradrop: Enabling lightweight multi-tenancy at the network’s extreme edge
Peng Liu, Dale Willis, and Suman Banerjee · 2016
Earlier work this paper cites.
Inter-layer per-mobile optimization of cloud mobile computing: a message-passing approach
Shahrouz Khalili and Osvaldo Simeone · 2016
Earlier work this paper cites.
Evaluation of plan quality assurance models for prostate cancer patients based on fully automatically generated pareto-optimal treatment plans
Yibing Wang, Sebastiaan Breedveld, Ben Heijmen, and Steven F Petit · 2016
Earlier work this paper cites.
Energy-efficient offloading for mobile edge computing in 5g heterogeneous networks
Ke Zhang, Yuming Mao, Supeng Leng, Quanxin Zhao, Longjiang Li, Xin Peng, Li Pan, Sabita Maharjan, and Yan Zhang · 2016
Earlier work this paper cites.
Edge computing: Vision and challenges
Weisong Shi, Jie Cao, Quan Zhang, Youhuizi Li, and Lanyu Xu · 2016
Earlier work this paper cites.
Incremental deployment and migration of geo-distributed situation awareness applications in the fog
Enrique Saurez, Kirak Hong, Dave Lillethun, Umakishore Ramachandran, and Beate Ottenwälder · 2016
Earlier work this paper cites.
3 ai trends for enterprise computing, 2017
Gartner · 2017
Earlier work this paper cites.
Qoe and power efficiency tradeoff for fog computing networks with fog node cooperation
Yong Xiao and Marwan Krunz · 2017
Earlier work this paper cites.
Mobility-aware application scheduling in fog computing
L. F. Bittencourt, J. Diaz-Montes, R. Buyya, O. F. Rana, and M. Parashar · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
Enorm: A framework for edge node resource management
Nan Wang, Blesson Varghese, Michail Matthaiou, and Dimitrios S Nikolopoulos · 2017
Earlier work this paper cites.
Edge-as-a-service: Towards distributed cloud architectures
Blesson Varghese, Nan Wang, Jianyu Li, and Dimitrios S Nikolopoulos · 2017
Earlier work this paper cites.
Class of service in fog computing
Judy C. Guevara, Luiz F. Bittencourt, and Nelson L. S. da Fonseca · 2017
Earlier work this paper cites.
Caching in the sky: Proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience
Mingzhe Chen, Mohammad Mozaffari, Walid Saad, Changchuan Yin, Mérouane Debbah, and Choong Seon Hong · 2017
Earlier work this paper cites.
Energy-latency tradeoff for energy-aware offloading in mobile edge computing networks
Jiao Zhang, Xiping Hu, Zhaolong Ning, Edith C-H Ngai, Li Zhou, Jibo Wei, Jun Cheng, and Bin Hu · 2017
Earlier work this paper cites.
Dynamic resource management across cloud-edge resources for performance-sensitive applications
Shashank Shekhar and Aniruddha Gokhale · 2017
Earlier work this paper cites.
Edge caching with mobility prediction in virtualized lte mobile networks
Andre S Gomes, Bruno Sousa, David Palma, Vitor Fonseca, Zhongliang Zhao, Edmundo Monteiro, Torsten Braun, Paulo Simoes, and Luis Cordeiro · 2017
Earlier work this paper cites.
A migration-enhanced edge computing support for mobile devices in hostile environments
Paolo Bellavista, Alessandro Zanni, and Michele Solimando · 2017
Earlier work this paper cites.
Follow me fog: Toward seamless handover timing schemes in a fog computing environment
Wei Bao, Dong Yuan, Zhengjie Yang, Shen Wang, Wei Li, Bing Bing Zhou, and Albert Y Zomaya · 2017
Earlier work this paper cites.
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar · 2017
Earlier work this paper cites.
A survey on multi-task learning
Yu Zhang and Qiang Yang · 2017
Earlier work this paper cites.
Scheduling in distributed systems: A cloud computing perspective
Luiz F. Bittencourt, Alfredo Goldman, Edmundo R. M. Madeira, Nelson L. S. da Fonseca, and Rizos Sakellariou · 2018
Earlier work this paper cites.
What edge computing means for infrastructure and operations leaders, 2018
Gartner · 2018
Earlier work this paper cites.
The internet of things, fog and cloud continuum: Integration and challenges
Luiz Bittencourt, Roger Immich, Rizos Sakellariou, Nelson Fonseca, Edmundo Madeira, Marilia Curado, Leandro Villas, Luiz DaSilva, Craig Lee, and Omer Rana · 2018
Earlier work this paper cites.
When edge meets learning: Adaptive control for resource-constrained distributed machine learning
Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K Leung, Christian Makaya, Ting He, and Kevin Chan · 2018
Earlier work this paper cites.
A taxonomy for management and optimization of multiple resources in edge computing
Klervie Toczé and Simin Nadjm-Tehrani · 2018
Earlier work this paper cites.
The internet of things, fog and cloud continuum: Integration and challenges
Luiz F. Bittencourt, Roger Immich, Rizos Sakellariou, Nelson L. S. da Fonseca, Edmundo R. M. Madeira, Marília Curado, Leandro Villas, Luiz A. DaSilva, Craig Lee, and Omer Rana · 2018
Earlier work this paper cites.
Federated meta-learning with fast convergence and efficient communication
Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, and Xiuqiang He · 2018
Earlier work this paper cites.
Edge computing in the industrial internet of things environment: Software-defined-networks-based edge-cloud interplay
K. Kaur, S. Garg, G. S. Aujla, N. Kumar, J. J. P. C. Rodrigues, and M. Guizani · 2018
Earlier work this paper cites.
Federated learning of predictive models from federated electronic health records
Theodora S Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch Paschalidis, and Wei Shi · 2018
Earlier work this paper cites.
Applied federated learning: Improving google keyboard query suggestions
Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Françoise Beaufays · 2018
Cited alongside, same era.
Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
Cited alongside, same era.
Federated learning with non-iid data
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra · 2018
Cited alongside, same era.
Kubeedge: An open platform to enable edge computing, 2018
Huawei · 2018
Cited alongside, same era.
Multi-objective optimization for edge device placement and reliable broadcasting in 5g nfv-based small cell networks
Federated learning for edge networks: Resource optimization and incentive mechanism
Latif U. Khan, Shashi Raj Pandey, Nguyen H. Tran, Walid Saad, Zhu Han, Minh N. H. Nguyen, and Choong Seon Hong · 2020
Later among the works it cites.
Federated edge learning: Design issues and challenges
Afaf Tak and Soumaya Cherkaoui · 2020
Later among the works it cites.
Edge intelligence: Architectures, challenges, and applications
Dianlei Xu, Tong Li, Yong Li, Xiang Su, Sasu Tarkoma, Tao Jiang, Jon Crowcroft, and Pan Hui · 2020
Later among the works it cites.
Federated learning: A survey on enabling technologies, protocols, and applications
Mohammed Aledhari, Rehma Razzak, Reza M Parizi, and Fahad Saeed · 2020
Later among the works it cites.
Federated learning in mobile edge networks: A comprehensive survey
Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, and Chunyan Miao · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hernani D. Chantre and Nelson L. S. da Fonseca · 2018
Cited alongside, same era.
edisco: Discovering edge nodes along the path
Aleksandr Zavodovski, Nitinder Mohan, and Jussi Kangasharju · 2018
Cited alongside, same era.
ECO: Harmonizing edge and cloud with ml/dl orchestration
Nisha Talagala, Swaminathan Sundararaman, Vinay Sridhar, Dulcardo Arteaga, Qianmei Luo, Sriram Subramanian, Sindhu Ghanta, Lior Khermosh, and Drew Roselli · 2018
Cited alongside, same era.
Videoedge: Processing camera streams using hierarchical clusters
Chien-Chun Hung, Ganesh Ananthanarayanan, Peter Bodik, Leana Golubchik, Minlan Yu, Paramvir Bahl, and Matthai Philipose · 2018
Cited alongside, same era.
Joint load balancing and offloading in vehicular edge computing and networks
Yueyue Dai, Du Xu, Sabita Maharjan, and Yan Zhang · 2018
Cited alongside, same era.
A survey on service migration in mobile edge computing
Shangguang Wang, Jinliang Xu, Ning Zhang, and Yujiong Liu · 2018
Cited alongside, same era.
Adaptive federated learning in resource constrained edge computing systems
Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis, Kin K Leung, Christian Makaya, Ting He, and Kevin Chan · 2019
Cited alongside, same era.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2019
Cited alongside, same era.
Federated learning in smart city sensing: Challenges and opportunities
Ji Chu Jiang, Burak Kantarci, Sema Oktug, and Tolga Soyata · 2020
Later among the works it cites.
Learnet: Reinforcement learning based flow scheduling for asynchronous deterministic networks
Jonathan Prados-Garzon, Tarik Taleb, and Miloud Bagaa · 2020
Later among the works it cites.
Federated learning for healthcare informatics
Jie Xu, Benjamin S Glicksberg, Chang Su, Peter Walker, Jiang Bian, and Fei Wang · 2020
Later among the works it cites.
Reliable federated learning for mobile networks
Jiawen Kang, Zehui Xiong, Dusit Niyato, Yuze Zou, Yang Zhang, and Mohsen Guizani · 2020
Later among the works it cites.
The future of digital health with federated learning
Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger R Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N Galtier, Bennett A Landman, Klaus Maier-Hein, et al · 2020
Later among the works it cites.
Federated deep reinforcement learning for internet of things with decentralized cooperative edge caching
Xiaofei Wang, Chenyang Wang, Xiuhua Li, Victor CM Leung, and Tarik Taleb · 2020
Later among the works it cites.
A secure federated transfer learning framework
Yang Liu, Yan Kang, Chaoping Xing, Tianjian Chen, and Qiang Yang · 2020
Later among the works it cites.
Multi-participant multi-class vertical federated learning
Siwei Feng and Han Yu · 2020
Later among the works it cites.
Asymmetrically vertical federated learning
Yang Liu, Xiong Zhang, and Libin Wang · 2020
Later among the works it cites.
Autonomics at the edge: Resource orchestration for edge native applications
I. Petri, O. Rana, L. F. Bittencourt, D. Balouek-Thomert, and M. Parashar · 2020
Later among the works it cites.
An edge computing node deployment method based on improved k-means clustering algorithm for smart manufacturing
Chun Jiang, Jiafu Wan, and Haider Abbas · 2020
Later among the works it cites.
On the classification of fog computing applications: A machine learning perspective
Judy C. Guevara, Ricardo da Silva Torres, and Nelson L. S. da Fonseca · 2020
Later among the works it cites.
Ibm federated learning: an enterprise framework white paper v0. 1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas, Yi Zhou, Ali Anwar, Shashank Rajamoni, Yuya Ong, Jayaram Radhakrishnan, Ashish Verma, Mathieu Sinn, et al · 2020
Later among the works it cites.
Multi-stage hybrid federated learning over large-scale d2d-enabled fog networks
S Hosseinalipour, SS Azam, CG Brinton, N Michelusi, V Aggarwal, DJ Love, and H Dai · 2020
Later among the works it cites.
Federated learning in vehicular edge computing: A selective model aggregation approach
Dongdong Ye, Rong Yu, Miao Pan, and Zhu Han · 2020
Later among the works it cites.
Attention-weighted federated deep reinforcement learning for device-to-device assisted heterogeneous collaborative edge caching
Xiaofei Wang, Ruibin Li, Chenyang Wang, Xiuhua Li, Tarik Taleb, and Victor CM Leung · 2020
Later among the works it cites.
Yae Jee Cho, Jianyu Wang, and Gauri Joshi · 2020
Later among the works it cites.
Energy efficient federated learning over wireless communication networks
Zhaohui Yang, Mingzhe Chen, Walid Saad, Choong Seon Hong, and Mohammad Shikh-Bahaei · 2020
Later among the works it cites.
Liang Li, Dian Shi, Ronghui Hou, Hui Li, Miao Pan, and Zhu Han · 2020
Later among the works it cites.
Energy-efficient radio resource allocation for federated edge learning
Qunsong Zeng, Yuqing Du, Kaibin Huang, and Kin K Leung · 2020
Later among the works it cites.
When deep reinforcement learning meets federated learning: Intelligent multi-timescale resource management for multi-access edge computing in 5g ultra dense network
Shuai Yu, Xu Chen, Zhi Zhou, Xiaowen Gong, and Di Wu · 2020
Later among the works it cites.
Personalized federated learning: A meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
Later among the works it cites.
Personalized federated learning for intelligent iot applications: A cloud-edge based framework
Qiong Wu, Kaiwen He, and Xu Chen · 2020
Later among the works it cites.
Survey of personalization techniques for federated learning
Viraj Kulkarni, Milind Kulkarni, and Aniruddha Pant · 2020
Later among the works it cites.
Data selection for federated learning with relevant and irrelevant data at clients
Tiffany Tuor, Shiqiang Wang, Bong Jun Ko, Changchang Liu, and Kin K Leung · 2020
Later among the works it cites.
Federated service chaining: Architecture and challenges
L. Cui, F. P. Tso, and W. Jia · 2020
Later among the works it cites.
Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems
Omar Abdel Wahab, Azzam Mourad, Hadi Otrok, and Tarik Taleb · 2021
Closest in time.
Federated edge learning: Design issues and challenges
Afaf Tak and Soumaya Cherkaoui · 2021
Closest in time.
Federated learning for 6g: Applications, challenges, and opportunities
Zhaohui Yang, Mingzhe Chen, Kai-Kit Wong, H Vincent Poor, and Shuguang Cui · 2021
Closest in time.
Federated learning for healthcare informatics
Jie Xu, Benjamin S Glicksberg, Chang Su, Peter Walker, Jiang Bian, and Fei Wang · 2021
Closest in time.
Ai-based resource management in beyond 5g cloud native environment
Abderrahmane Boudi, Miloud Bagaa, Petteri Pöyhönen, Tarik Taleb, and Hannu Flinck · 2021
Closest in time.
Simedgeintel: A open-source simulation platform for resource management in edge intelligence
Chenyang Wang, Ruibin Li, Wenkai Li, Chao Qiu, and Xiaofei Wang · 2021
Closest in time.
Task scheduling in cloud-fog computing systems
Judy C. Guevara and Nelson L. S. da Fonseca · 2021
Closest in time.
Deploying federated learning in large-scale cellular networks: Spatial convergence analysis
Zhenyi Lin, Xiaoyang Li, Vincent KN Lau, Yi Gong, and Kaibin Huang · 2021
Closest in time.
Energy-aware resource management for federated learning in multi-access edge computing systems
Chit Wutyee Zaw, Shashi Raj Pandey, Kitae Kim, and Choong Seon Hong · 2021
Closest in time.
A decentralized game theoretic approach for energy-aware resource management in federated learning
Chit Wutyee Zaw and Choong Seon Hong · 2021
Closest in time.