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ORCIDCong Shen 0001
Person information
- affiliation: University of Virginia, Charles L. Brown Department of Electrical and Computer Engineering, Charlottesville, VA, USA
- affiliation: University of Science and Technology of China, Department of Electronic Engineering and Information Science, Department of Electronic Engineering and Information Science, Hefei, China
- affiliation (former): Qualcomm Research, San Diego, CA, USA
- affiliation: University of California Los Angeles, UCLA, Electrical Engineering Department, CA, USA
Other persons with the same name
- Cong Shen — disambiguation page
- Cong Shen 0002
![[0000-0001-8505-6406]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0001-8505-6406")
— Hunan University, College of Computer Science and Electronic Engineering, Changsha, China - Cong Shen 0004 — Chongqing University, Department of Industrial Engineering, China (and 1 more)
- Cong Shen 0005 — University of Kassel, Department of Energy Management and Power System Operation, Germany
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2020 – today
- 2024
[j45]Shengbo Chen, Le Li, Guanghui Wang, Meng Pang, Cong Shen:
Federated Learning With Heterogeneous Quantization Bit Allocation and Aggregation for Internet of Things. IEEE Internet Things J. 11(2): 3132-3143 (2024)- [j44]Byungjoon Bae, Doeon Lee, Minseong Park, Yujia Mu, Yongmin Baek, Inbo Sim, Cong Shen, Kyusang Lee:
Stereoscopic artificial compound eyes for spatiotemporal perception in three-dimensional space. Sci. Robotics 9(90) (2024) - [j43]Chengshuai Shi, Ruida Zhou, Kun Yang, Cong Shen:
Harnessing the Power of Federated Learning in Federated Contextual Bandits. Trans. Mach. Learn. Res. 2024 (2024) - [j42]Jieming Bian, Lei Wang, Kun Yang, Cong Shen, Jie Xu:
Accelerating Hybrid Federated Learning Convergence Under Partial Participation. IEEE Trans. Signal Process. 72: 3258-3271 (2024) - [j41]Xizixiang Wei, Cong Shen, Jing Yang, H. Vincent Poor:
Random Orthogonalization for Federated Learning in Massive MIMO Systems. IEEE Trans. Wirel. Commun. 23(3): 2469-2485 (2024) - [c81]Yujia Mu, Xizixiang Wei, Cong Shen:
An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning. ICC 2024: 5565-5570 - [c80]Renpu Liu, Cong Shen, Jing Yang:
Federated Representation Learning in the Under-Parameterized Regime. ICML 2024 - [c79]Sanjay Purushotham, Dongjin Song, Qingsong Wen, Jun Huan, Cong Shen, Stefan Zohren, Yuriy Nevmyvaka:
The 10th Mining and Learning from Time Series Workshop: From Classical Methods to LLMs. KDD 2024: 6733-6734 - [c78]Ye Xing, Jun Huan, Wee Hyong Tok, Cong Shen, Johannes Gehrke, Katherine Lin, Arjun Guha, Omer Tripp, Murali Krishna Ramanathan:
NL2Code-Reasoning and Planning with LLMs for Code Development. KDD 2024: 6745-6746 - [i59]Chengshuai Shi, Kun Yang, Jing Yang, Cong Shen:
Best Arm Identification for Prompt Learning under a Limited Budget. CoRR abs/2402.09723 (2024) - [i58]Yujia Mu, Xizixiang Wei, Cong Shen:
An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning. CoRR abs/2404.09392 (2024) - [i57]Renpu Liu, Cong Shen, Jing Yang:
Federated Representation Learning in the Under-Parameterized Regime. CoRR abs/2406.04596 (2024) - [i56]Boyuan Li, Zihao Peng, Yafei Li, Mingliang Xu, Shengbo Chen, Baofeng Ji, Cong Shen:
Neighborhood and Global Perturbations Supported SAM in Federated Learning: From Local Tweaks To Global Awareness. CoRR abs/2408.14144 (2024) - 2023
- [j40]Kun Yang, Shengbo Chen, Cong Shen:
On the Convergence of Hybrid Server-Clients Collaborative Training. IEEE J. Sel. Areas Commun. 41(3): 802-819 (2023) - [j39]Zihao Peng, Boyuan Li, Le Li, Shengbo Chen, Guanghui Wang, Hong Rao, Cong Shen:
Performance Optimization for Noise Interference Privacy Protection in Federated Learning. IEEE Trans. Cogn. Commun. Netw. 9(5): 1322-1339 (2023) - [j38]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Reward Teaching for Federated Multiarmed Bandits. IEEE Trans. Signal Process. 71: 4407-4422 (2023) - [c77]Kun Yang, Cong Shen, Jing Yang, Shu-Ping Yeh, Jerry Sydir:
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets. ACSSC 2023: 629-633 - [c76]Jieming Bian, Cong Shen, Jie Xu:
Federated Learning via Indirect Server-Client Communications. CISS 2023: 1-5 - [c75]Kun Yang, Chengshuai Shi, Cong Shen:
Teaching Reinforcement Learning Agents via Reinforcement Learning. CISS 2023: 1-6 - [c74]Yujia Mu, Cong Shen:
Communication and Storage Efficient Federated Split Learning. ICC 2023: 2976-2981 - [c73]Xizixiang Wei, Tianhao Wang, Ruiquan Huang, Cong Shen, Jing Yang, H. Vincent Poor:
FLORAS: Differentially Private Wireless Federated Learning Using Orthogonal Sequences. ICC 2023: 3121-3126 - [c72]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, Tong Zhang:
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. ICLR 2023 - [c71]Donghao Li, Ruiquan Huang, Cong Shen, Jing Yang:
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints. ICML 2023: 19527-19564 - [c70]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources. ICML 2023: 31353-31388 - [c69]Renpu Liu, Jing Yang, Cong Shen:
Exploiting Feature Heterogeneity for Improved Generalization in Federated Multi-task Learning. ISIT 2023: 180-185 - [c68]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Reward Teaching for Federated Multi-armed Bandits. ISIT 2023: 1454-1459 - [c67]Chengshuai Shi, Cong Shen, Nicholas D. Sidiropoulos:
On High-dimensional and Low-rank Tensor Bandits. ISIT 2023: 1460-1465 - [c66]Sanjay Purushotham, Dongjin Song, Qingsong Wen, Jun Huan, Cong Shen, Yuriy Nevmyvaka:
The 9th SIGKDD International Workshop on Mining and Learning from Time Series. KDD 2023: 5876-5877 - [c65]Li Fan, Ruida Zhou, Chao Tian, Cong Shen:
Federated Linear Bandits with Finite Adversarial Actions. NeurIPS 2023 - [i55]Yujia Mu, Cong Shen:
Communication and Storage Efficient Federated Split Learning. CoRR abs/2302.05599 (2023) - [i54]Jieming Bian, Cong Shen, Jie Xu:
Federated Learning via Indirect Server-Client Communications. CoRR abs/2302.07323 (2023) - [i53]Jieming Bian, Lei Wang, Kun Yang, Cong Shen, Jie Xu:
Accelerating Hybrid Federated Learning Convergence under Partial Participation. CoRR abs/2304.05397 (2023) - [i52]Jieming Bian, Cong Shen, Jie Xu:
Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning. CoRR abs/2304.10744 (2023) - [i51]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Reward Teaching for Federated Multi-armed Bandits. CoRR abs/2305.02441 (2023) - [i50]Chengshuai Shi, Cong Shen, Nicholas D. Sidiropoulos:
On High-dimensional and Low-rank Tensor Bandits. CoRR abs/2305.03884 (2023) - [i49]Donghao Li, Ruiquan Huang, Cong Shen, Jing Yang:
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints. CoRR abs/2306.06265 (2023) - [i48]Xizixiang Wei, Tianhao Wang, Ruiquan Huang, Cong Shen, Jing Yang, H. Vincent Poor:
Differentially Private Wireless Federated Learning Using Orthogonal Sequences. CoRR abs/2306.08280 (2023) - [i47]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources. CoRR abs/2306.08364 (2023) - [i46]Li Fan, Ruida Zhou, Chao Tian, Cong Shen:
Federated Linear Bandits with Finite Adversarial Actions. CoRR abs/2311.00973 (2023) - [i45]Kun Yang, Cong Shen, Jing Yang, Shu-Ping Yeh, Jerry Sydir:
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets. CoRR abs/2311.11423 (2023) - [i44]Kun Yang, Shu-Ping Yeh, Menglei Zhang, Jerry Sydir, Jing Yang, Cong Shen:
Advancing RAN Slicing with Offline Reinforcement Learning. CoRR abs/2312.10547 (2023) - [i43]Chengshuai Shi, Ruida Zhou, Kun Yang, Cong Shen:
Harnessing the Power of Federated Learning in Federated Contextual Bandits. CoRR abs/2312.16341 (2023) - 2022
- [j37]Cong Shen, Zhaozhi Qian, Alihan Hüyük, Mihaela van der Schaar:
MARS: Assisting Human with Information Processing Tasks Using Machine Learning. ACM Trans. Comput. Heal. 3(2): 21:1-21:19 (2022) - [j36]Xizixiang Wei, Cong Shen:
Federated Learning Over Noisy Channels: Convergence Analysis and Design Examples. IEEE Trans. Cogn. Commun. Netw. 8(2): 1253-1268 (2022) - [j35]Yifei Li, Yijia Guo, Mamoun Alazab, Shengbo Chen, Cong Shen, Keping Yu:
Joint Optimal Quantization and Aggregation of Federated Learning Scheme in VANETs. IEEE Trans. Intell. Transp. Syst. 23(10): 19852-19863 (2022) - [j34]Wenjing Chen, Ruida Zhou, Chao Tian, Cong Shen:
On Top-k Selection From m-Wise Partial Rankings via Borda Counting. IEEE Trans. Signal Process. 70: 2031-2045 (2022) - [j33]Xizixiang Wei, Yi Jiang, Xin Wang, Cong Shen:
Tx-Rx Reciprocity Calibration for Hybrid Massive MIMO Systems. IEEE Wirel. Commun. Lett. 11(2): 431-435 (2022) - [c64]Kun Yang, Donghao Li, Cong Shen, Jing Yang, Shu-Ping Yeh, Jerry Sydir:
Multi-Agent Reinforcement Learning for Wireless User Scheduling: Performance, Scalablility, and Generalization. IEEECONF 2022: 1169-1174 - [c63]Kun Yang, Cong Shen:
On the Convergence of Hybrid Federated Learning with Server-Clients Collaborative Training. CISS 2022: 252-257 - [c62]Cong Shen, Jing Yang, Jie Xu:
On Federated Learning with Energy Harvesting Clients. ICASSP 2022: 8657-8661 - [c61]Xizixiang Wei, Cong Shen, Jing Yang, H. Vincent Poor:
Random Orthogonalization for Federated Learning in Massive MIMO Systems. ICC 2022: 3382-3387 - [c60]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang:
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games. ICML 2022: 24496-24523 - [c59]Zhihui Shao, Jianyi Yang, Cong Shen, Shaolei Ren:
Learning for Robust Combinatorial Optimization: Algorithm and Application. INFOCOM 2022: 930-939 - [c58]Yujia Mu, Cong Shen, Yonina C. Eldar:
Optimizing Federated Averaging over Fading Channels. ISIT 2022: 1277-1281 - [c57]Duo Cheng, Ruiquan Huang, Cong Shen, Jing Yang:
Cascading Bandits with Two-Level Feedback. ISIT 2022: 1892-1896 - [c56]Sanjay Purushotham, Jun Huan, Cong Shen, Dongjin Song, Yuyang Wang, Jan Gasthaus, Hilaf Hasson, Youngsuk Park, Sungyong Seo, Yuriy Nevmyvaka:
8th SIGKDD International Workshop on Mining and Learning from Time Series - Deep Forecasting: Models, Interpretability, and Applications. KDD 2022: 4896-4897 - [i42]Xizixiang Wei, Cong Shen, Jing Yang, H. Vincent Poor:
Random Orthogonalization for Federated Learning in Massive MIMO Systems. CoRR abs/2201.12490 (2022) - [i41]Cong Shen, Jing Yang, Jie Xu:
On Federated Learning with Energy Harvesting Clients. CoRR abs/2202.06105 (2022) - [i40]Wenjing Chen, Ruida Zhou, Chao Tian, Cong Shen:
On Top-k Selection from m-wise Partial Rankings via Borda Counting. CoRR abs/2204.05742 (2022) - [i39]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, Tong Zhang:
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. CoRR abs/2205.15512 (2022) - [i38]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang:
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games. CoRR abs/2210.01907 (2022) - [i37]Xizixiang Wei, Cong Shen, Jing Yang, H. Vincent Poor:
Random Orthogonalization for Federated Learning in Massive MIMO Systems. CoRR abs/2210.09881 (2022) - 2021
- [j32]Cong Shen, Jie Xu, Sihui Zheng, Xiang Chen:
Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges. IEEE Commun. Mag. 59(5): 82-87 (2021) - [j31]Sihui Zheng, Cong Shen, Xiang Chen:
Design and Analysis of Uplink and Downlink Communications for Federated Learning. IEEE J. Sel. Areas Commun. 39(7): 2150-2167 (2021) - [j30]Chengshuai Shi, Cong Shen:
On No-Sensing Adversarial Multi-Player Multi-Armed Bandits With Collision Communications. IEEE J. Sel. Areas Inf. Theory 2(2): 515-533 (2021) - [j29]Lixing Chen, Cong Shen, Pan Zhou, Jie Xu:
Collaborative Service Placement for Edge Computing in Dense Small Cell Networks. IEEE Trans. Mob. Comput. 20(2): 377-390 (2021) - [j28]Chengshuai Shi, Cong Shen:
Multi-Player Multi-Armed Bandits With Collision-Dependent Reward Distributions. IEEE Trans. Signal Process. 69: 4385-4402 (2021) - [j27]Shengbo Chen, Cong Shen, Lanxue Zhang, Yuanmin Tang:
Dynamic Aggregation for Heterogeneous Quantization in Federated Learning. IEEE Trans. Wirel. Commun. 20(10): 6804-6819 (2021) - [c55]Chengshuai Shi, Cong Shen:
Federated Multi-Armed Bandits. AAAI 2021: 9603-9611 - [c54]Chengshuai Shi, Cong Shen, Jing Yang:
Federated Multi-armed Bandits with Personalization. AISTATS 2021: 2917-2925 - [c53]Hyun-Suk Lee, Cong Shen, William R. Zame, Jang-Won Lee, Mihaela van der Schaar:
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups. AISTATS 2021: 2980-2988 - [c52]Yujia Mu, Yuanlong Tan, Malathi Veeraraghavan, Cong Shen:
A Machine Learning Approach for Rate Prediction in Multicast File-stream Distribution Networks. GLOBECOM 2021: 1-6 - [c51]Cong Shen, Pengkai Zhao, Xiliang Luo:
On Energy Efficient Uplink Multi-User MIMO with Shared LNA Control. ICC 2021: 1-6 - [c50]Xizixiang Wei, Cong Shen:
Federated Learning over Noisy Channels. ICC 2021: 1-6 - [c49]Sihui Zheng, Cong Shen, Xiang Chen:
Design and Analysis of Uplink and Downlink Communications for Federated Learning. ICC 2021: 1-6 - [c48]Chengshuai Shi, Cong Shen:
An Attackability Perspective on No-Sensing Adversarial Multi-player Multi-armed Bandits. ISIT 2021: 533-538 - [c47]Chengshuai Shi, Haifeng Xu, Wei Xiong, Cong Shen:
(Almost) Free Incentivized Exploration from Decentralized Learning Agents. NeurIPS 2021: 560-571 - [c46]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization. NeurIPS 2021: 22392-22404 - [c45]Ruiquan Huang, Weiqiang Wu, Jing Yang, Cong Shen:
Federated Linear Contextual Bandits. NeurIPS 2021: 27057-27068 - [i36]Xizixiang Wei, Cong Shen:
Federated Learning over Noisy Channels: Convergence Analysis and Design Examples. CoRR abs/2101.02198 (2021) - [i35]Hyun-Suk Lee, Cong Shen, William R. Zame, Jang-Won Lee, Mihaela van der Schaar:
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups. CoRR abs/2101.10998 (2021) - [i34]Chengshuai Shi, Cong Shen:
Federated Multi-Armed Bandits. CoRR abs/2101.12204 (2021) - [i33]Chengshuai Shi, Cong Shen, Jing Yang:
Federated Multi-armed Bandits with Personalization. CoRR abs/2102.13101 (2021) - [i32]Cong Shen, Jie Xu, Sihui Zheng, Xiang Chen:
Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges. CoRR abs/2104.06990 (2021) - [i31]Chengshuai Shi, Cong Shen:
Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions. CoRR abs/2106.13669 (2021) - [i30]Ruiquan Huang, Weiqiang Wu, Jing Yang, Cong Shen:
Federated Linear Contextual Bandits. CoRR abs/2110.14177 (2021) - [i29]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization. CoRR abs/2110.14622 (2021) - [i28]Chengshuai Shi, Haifeng Xu, Wei Xiong, Cong Shen:
(Almost) Free Incentivized Exploration from Decentralized Learning Agents. CoRR abs/2110.14628 (2021) - [i27]Zhihui Shao, Jianyi Yang, Cong Shen, Shaolei Ren:
Learning for Robust Combinatorial Optimization: Algorithm and Application. CoRR abs/2112.10377 (2021) - 2020
- [j26]Shengbo Chen, Lanxue Zhang, Cong Shen, Keping Yu, San Hlaing Myint, Zheng Wen:
On Scheduling Policies With Heavy-Tailed Dynamics in Wireless Queueing Systems. IEEE Access 8: 32137-32149 (2020) - [j25]Fei Liang, Cong Shen, Wei Yu, Feng Wu:
Towards Optimal Power Control via Ensembling Deep Neural Networks. IEEE Trans. Commun. 68(3): 1760-1776 (2020) - [j24]Chao Gan, Ruida Zhou, Jing Yang, Cong Shen:
Cost-Aware Cascading Bandits. IEEE Trans. Signal Process. 68: 3692-3706 (2020) - [j23]Xianzhe Xu, Meixia Tao, Cong Shen:
Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Caching. IEEE Trans. Wirel. Commun. 19(4): 2570-2585 (2020) - [j22]Saichao Liu, Shengbo Chen, Cong Shen, Mohamed A. Ismail, Roshan Kumar:
Improved Low-Resolution Quantized SIMO Estimation via Deep Learning. IEEE Wirel. Commun. Lett. 9(8): 1331-1335 (2020) - [c44]Chao Gan, Jing Yang, Cong Shen:
Thresholded Wirtinger Flow for Fast Millimeter Wave Beam Alignment. ACSSC 2020: 32-36 - [c43]Cong Shen, Donghao Li, Jing Yang:
MIMO Receive Antenna Selection via Deep Learning and Greedy Adaptation. ACSSC 2020: 403-407 - [c42]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Decentralized Multi-player Multi-armed Bandits with No Collision Information. AISTATS 2020: 1519-1528 - [c41]Weiqiang Wu, Jing Yang, Cong Shen:
Stochastic Linear Contextual Bandits with Diverse Contexts. AISTATS 2020: 2392-2401 - [c40]Hyun-Suk Lee, Cong Shen, James Jordon, Mihaela van der Schaar:
Contextual Constrained Learning for Dose-Finding Clinical Trials. AISTATS 2020: 2645-2654 - [c39]Cong Shen, Zhiyang Wang, Sofia S. Villar, Mihaela van der Schaar:
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints. ICML 2020: 8730-8740 - [c38]Cong Shen, Shengbo Chen:
Federated Learning with Heterogeneous Quantization. SEC 2020: 405-409 - [c37]Kun Yang, Cong Shen, Tie Liu:
Deep Reinforcement Learning based Wireless Network Optimization: A Comparative Study. INFOCOM Workshops 2020: 1248-1253 - [c36]Wenjing Chen, Ruida Zhou, Chao Tian, Cong Shen:
On Top-k Selection from m-wise Partial Rankings via Borda Counting. ISIT 2020: 2759-2764 - [c35]Chao Gan, Ruida Zhou, Jing Yang, Cong Shen:
Cost-Aware Learning and Optimization for Opportunistic Spectrum Access. ITA 2020: 1-12 - [c34]Hyun-Suk Lee, Yao Zhang, William R. Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar:
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification. NeurIPS 2020 - [i26]Hyun-Suk Lee, Cong Shen, James Jordon, Mihaela van der Schaar:
Contextual Constrained Learning for Dose-Finding Clinical Trials. CoRR abs/2001.02463 (2020) - [i25]Xianzhe Xu, Meixia Tao, Cong Shen:
Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Caching. CoRR abs/2001.03835 (2020) - [i24]Chengshuai Shi, Wei Xiong, Cong Shen, Jing Yang:
Decentralized Multi-player Multi-armed Bandits with No Collision Information. CoRR abs/2003.00162 (2020) - [i23]Weiqiang Wu, Jing Yang, Cong Shen:
Stochastic Linear Contextual Bandits with Diverse Contexts. CoRR abs/2003.02681 (2020) - [i22]Cong Shen, Zhiyang Wang, Sofia S. Villar, Mihaela van der Schaar:
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints. CoRR abs/2006.05026 (2020) - [i21]Hyun-Suk Lee, Yao Zhang, William R. Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar:
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification. CoRR abs/2006.07917 (2020) - [i20]Chengshuai Shi, Cong Shen:
On No-Sensing Adversarial Multi-player Multi-armed Bandits with Collision Communications. CoRR abs/2011.01090 (2020) - [i19]Sihui Zheng, Cong Shen, Xiang Chen:
Design and Analysis of Uplink and Downlink Communications for Federated Learning. CoRR abs/2012.04057 (2020)
2010 – 2019
- 2019
- [j21]Wenyi Zhang, Yizhu Wang, Cong Shen, Ning Liang:
A Regression Approach to Certain Information Transmission Problems. IEEE J. Sel. Areas Commun. 37(11): 2517-2531 (2019) - [j20]Chao Gan, Ruida Zhou, Jing Yang, Cong Shen:
Cost-Aware Learning and Optimization for Opportunistic Spectrum Access. IEEE Trans. Cogn. Commun. Netw. 5(1): 15-27 (2019) - [j19]