Learning for Learning: Predictive Online Control of Federated Learning with Edge Provisioning
暂无分享,去创建一个
Zhuzhong Qian | Lei Jiao | Sanglu Lu | Yibo Jin | Sheng Zhang | Sanglu Lu | Zhuzhong Qian | Sheng Zhang | Lei Jiao | Yibo Jin
[1] Walid Saad,et al. A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks , 2021, IEEE Transactions on Wireless Communications.
[2] Walid Saad,et al. Energy Efficient Federated Learning Over Wireless Communication Networks , 2019, IEEE Transactions on Wireless Communications.
[3] Peter Richtárik,et al. Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.
[4] Xiang Li,et al. On the Convergence of FedAvg on Non-IID Data , 2019, ICLR.
[5] A. Anonymous,et al. Consumer Data Privacy in a Networked World: A Framework for Protecting Privacy and Promoting Innovation in the Global Digital Economy , 2013, J. Priv. Confidentiality.
[6] Shuai Yu,et al. CEFL: Online Admission Control, Data Scheduling, and Accuracy Tuning for Cost-Efficient Federated Learning Across Edge Nodes , 2020, IEEE Internet of Things Journal.
[7] Minghua Chen,et al. Online energy generation scheduling for microgrids with intermittent energy sources and co-generation , 2012, SIGMETRICS '13.
[8] Xu Chen,et al. Adaptive User-managed Service Placement for Mobile Edge Computing: An Online Learning Approach , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.
[9] Zhuzhong Qian,et al. Provisioning Edge Inference as a Service via Online Learning , 2020, 2020 17th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).
[10] Michael I. Jordan,et al. CoCoA: A General Framework for Communication-Efficient Distributed Optimization , 2016, J. Mach. Learn. Res..
[11] Lei Lei,et al. Resource Allocation Based on Deep Reinforcement Learning in IoT Edge Computing , 2020, IEEE Journal on Selected Areas in Communications.
[12] Sanglu Lu,et al. Resource-Efficient and Convergence-Preserving Online Participant Selection in Federated Learning , 2020, 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS).
[13] Rajiv Gandhi,et al. Dependent rounding and its applications to approximation algorithms , 2006, JACM.
[15] Carlee Joe-Wong,et al. Network-Aware Optimization of Distributed Learning for Fog Computing , 2020, IEEE/ACM Transactions on Networking.
[16] Xiongwen Zhao,et al. Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT , 2020, IEEE Internet of Things Journal.
[17] Jun Li,et al. Multiple Granularity Online Control of Cloudlet Networks for Edge Computing , 2018, 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).
[18] Chao Xu,et al. Dedas: Online Task Dispatching and Scheduling with Bandwidth Constraint in Edge Computing , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.
[19] Ramesh K. Sitaraman,et al. The Akamai network: a platform for high-performance internet applications , 2010, OPSR.
[20] Hubert Eichner,et al. Towards Federated Learning at Scale: System Design , 2019, SysML.
[21] Ramesh K. Sitaraman,et al. A TTL-based Approach for Data Aggregation in Geo-distributed Streaming Analytics , 2019, Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems.
[22] Minlan Yu,et al. Wide-area analytics with multiple resources , 2018, EuroSys.
[23] Zhi Zhou,et al. CE-IoT: Cost-Effective Cloud-Edge Resource Provisioning for Heterogeneous IoT Applications , 2020, IEEE Internet of Things Journal.
[24] Patrick Jaillet,et al. Online Learning with a Hint , 2017, NIPS.
[25] Minghua Chen,et al. Moving Big Data to The Cloud: An Online Cost-Minimizing Approach , 2013, IEEE Journal on Selected Areas in Communications.
[26] Jie Xu,et al. Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense Networks , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.
[27] Michael I. Jordan,et al. Distributed optimization with arbitrary local solvers , 2015, Optim. Methods Softw..
[28] Lachlan L. H. Andrew,et al. Online Convex Optimization Using Predictions , 2015, SIGMETRICS.
[29] Lachlan L. H. Andrew,et al. Dynamic right-sizing for power-proportional data centers , 2011, 2011 Proceedings IEEE INFOCOM.
[30] Kin K. Leung,et al. Adaptive Federated Learning in Resource Constrained Edge Computing Systems , 2018, IEEE Journal on Selected Areas in Communications.
[31] Yong Liu,et al. Real-time bandwidth prediction and rate adaptation for video calls over cellular networks , 2016, MMSys.
[32] Albert Y. Zomaya,et al. Federated Learning over Wireless Networks: Optimization Model Design and Analysis , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.
[33] Yongbo Li,et al. A Reinforcement Learning Approach for Online Service Tree Placement in Edge Computing , 2019, 2019 IEEE 27th International Conference on Network Protocols (ICNP).
[34] Yunxin Liu,et al. Learning-Aided Stochastic Network Optimization With State Prediction , 2018, IEEE/ACM Transactions on Networking.
[35] Mehryar Mohri,et al. Agnostic Federated Learning , 2019, ICML.
[36] Jiacheng Chen,et al. Dynamic Task Offloading and Resource Allocation for Mobile-Edge Computing in Dense Cloud RAN , 2020, IEEE Internet of Things Journal.
[37] Carlo Curino,et al. Global Analytics in the Face of Bandwidth and Regulatory Constraints , 2015, NSDI.
[38] Adam Wierman,et al. Using Predictions in Online Optimization: Looking Forward with an Eye on the Past , 2016, SIGMETRICS.
[39] H. Vincent Poor,et al. Scheduling Policies for Federated Learning in Wireless Networks , 2019, IEEE Transactions on Communications.
[40] Farzin Haddadpour,et al. On the Convergence of Local Descent Methods in Federated Learning , 2019, ArXiv.