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Heterogeneity-Aware Memory Efficient Federated Learning via Progressive Layer Freezing Conference paper
Wu, Yebo, Li, Li, Tian, Chunlin, Chang, Tao, Lin, Chi, Wang, Cong, Xu, Cheng Zhong. Heterogeneity-Aware Memory Efficient Federated Learning via Progressive Layer Freezing[C]:Institute of Electrical and Electronics Engineers Inc., 2024.
Authors:  Wu, Yebo;  Li, Li;  Tian, Chunlin;  Chang, Tao;  Lin, Chi; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0 | Submit date：2024/11/05
Federated Learning  Heterogeneous Memory  On-device Training  Training  Accuracy  Runtime  Perturbation Methods  Memory Management  Quality Of Service  
Heterogeneity-Aware Coordination for Federated Learning via Stitching Pre-trained blocks Conference paper
Zhan, Shichen, Wu, Yebo, Tian, Chunlin, Zhao, Yan, Li, Li. Heterogeneity-Aware Coordination for Federated Learning via Stitching Pre-trained blocks[C]:Institute of Electrical and Electronics Engineers Inc., 2024.
Authors:  Zhan, Shichen;  Wu, Yebo;  Tian, Chunlin;  Zhao, Yan;  Li, Li
Favorite | TC[WOS]:0 TC[Scopus]:1 | Submit date：2024/11/05
Federated Learning  Pre-training  Resource-efficient  Training  Performance Evaluation  Energy Consumption  Accuracy  Memory Management  Quality Of Service  
FedMG: A Federated Multi-Global Optimization Framework for Autonomous Driving Control Conference paper
Ma, Jialiang, Tian, Chunlin, Li, Li, Xu, Chengzhong. FedMG: A Federated Multi-Global Optimization Framework for Autonomous Driving Control[C]:Institute of Electrical and Electronics Engineers Inc., 2024.
Authors:  Ma, Jialiang;  Tian, Chunlin;  Li, Li;  Xu, Chengzhong
Favorite | TC[WOS]:0 TC[Scopus]:2 | Submit date：2024/11/05
Training  Federated Learning  Velocity Control  Process Control  Collaboration  Quality Of Service  Distance Measurement  Autonomous Driving  Federated Learning  Control Optimization  
A Reconfigurable Floating-Point Compute-In-Memory With Analog Exponent Pre-Processes Journal article
He, Pengyu, Zhao, Yuanzhe, Xie, Heng, Wang, Yang, Yin, Shouyi, Li, Li, Zhu, Yan, Martins, Rui P., Chan, Chi Hang, Zhang, Minglei. A Reconfigurable Floating-Point Compute-In-Memory With Analog Exponent Pre-Processes[J]. IEEE Solid-State Circuits Letters, 2024, 7, 271-274.
Authors:  He, Pengyu;  Zhao, Yuanzhe;  Xie, Heng;  Wang, Yang;  Yin, Shouyi; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0 | Submit date：2024/10/10
Compute-in-memory Macro(Cim)  Exponent Pre-process  Floating-point(Fp)  Reconfigurable  Segmented Computation  
FedGCS: A Generative Framework for Effcient Client Selection in Federated Learning via Gradient-based Optimization Conference paper
ZHIYUAN NING, CHUNLIN TIAN, MENG XIAO, WEI FAN, PENGYANG WANG, LI LI, PENGFEI WANG, YUANCHUN ZHOU. FedGCS: A Generative Framework for Effcient Client Selection in Federated Learning via Gradient-based Optimization[C], 2024.
Authors:  ZHIYUAN NING;  CHUNLIN TIAN;  MENG XIAO;  WEI FAN;  PENGYANG WANG; et al.
Favorite |  | Submit date：2024/08/28
Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning Journal article
Chunlin Tian, Zhan Shi, Xinpeng Qin, Li Li, ChengZhong Xu. Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning[J]. International Conference on Machine Learning, 2024, 235, 48211 - 48225.
Authors:  Chunlin Tian;  Zhan Shi;  Xinpeng Qin;  Li Li;  ChengZhong Xu
Favorite | TC[Scopus]:1 | Submit date：2024/08/29
A 28nm 314.6TLFOPS/W Reconfigurable Floating-Point Analog Compute-In-Memory Macro with Exponent Approximation and Two-Stage Sharing TD-ADC Conference paper
He, Pengyu, Zhao, Yuanzhe, Xie, Heng, Wang, Yang, Yin, Shouyi, Li, Li, Zhu, Yan, Martins, R. P., Chan, Chi Hang, Zhang, Minglei. A 28nm 314.6TLFOPS/W Reconfigurable Floating-Point Analog Compute-In-Memory Macro with Exponent Approximation and Two-Stage Sharing TD-ADC[C]:Institute of Electrical and Electronics Engineers Inc., 2024, 199537.
Authors:  He, Pengyu;  Zhao, Yuanzhe;  Xie, Heng;  Wang, Yang;  Yin, Shouyi; et al.
Favorite | TC[WOS]:0 TC[Scopus]:1 | Submit date：2024/06/05
FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization Conference paper
Ning, Zhiyuan, Tian, Chunlin, Xiao, Meng, Fan, Wei, Wang, Pengyang, Li, Li, Wang, Pengfei, Zhou, Yuanchun. FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization[C]:International Joint Conferences on Artificial Intelligence, 2024, 4760-4768.
Authors:  Ning, Zhiyuan;  Tian, Chunlin;  Xiao, Meng;  Fan, Wei;  Wang, Pengyang; et al.
Favorite | TC[Scopus]:0 | Submit date：2024/10/10
Machine Learning  Data Mining  
AutoRS: Environment-Dependent Real-Time Scheduling for End-to-End Autonomous Driving Journal article
Ma, Jialiang, Li, Li, Xu, Chengzhong. AutoRS: Environment-Dependent Real-Time Scheduling for End-to-End Autonomous Driving[J]. IEEE Transactions on Parallel and Distributed Systems, 2023, 34(12), 3238-3252.
Authors:  Ma, Jialiang;  Li, Li;  Xu, Chengzhong
Favorite | TC[WOS]:0 TC[Scopus]:3  IF:5.6/4.5 | Submit date：2024/01/02
Autonomous Driving  Real-time Scheduling  
Federated Noisy Client Learning Journal article
Tam Ka Hou, Li Li, Bo Han, ChengZhong Xu, HuaZhu Fu. Federated Noisy Client Learning[J]. Transactions on Neural Networks and Learning Systems, 2023.
Authors:  Tam Ka Hou;  Li Li;  Bo Han;  ChengZhong Xu;  HuaZhu Fu
Favorite |  | Submit date：2023/12/14