AdaCoOpt: Leverage the interplay of batch size and aggregation frequency for federated learning
Federated Learning (FL) is a distributed learning paradigm that can coordinate
heterogeneous edge devices to perform model training without sharing private raw data …
heterogeneous edge devices to perform model training without sharing private raw data …
[PDF][PDF] AdaCoOpt: Leverage the Interplay of Batch Size and Aggregation Frequency for Federated Learning
W Liu, X Zhang, J Duan, C Joe-Wong, Z Zhou, X Chen - 0xxz.github.io
Federated Learning (FL) is a distributed learning paradigm that can coordinate
heterogeneous edge devices to perform model training without sharing private raw data …
heterogeneous edge devices to perform model training without sharing private raw data …
[PDF][PDF] AdaCoOpt: Leverage the Interplay of Batch Size and Aggregation Frequency for Federated Learning
W Liu, X Zhang, J Duan, C Joe-Wong, Z Zhou, X Chen - research.ece.cmu.edu
Federated Learning (FL) is a distributed learning paradigm that can coordinate
heterogeneous edge devices to perform model training without sharing private raw data …
heterogeneous edge devices to perform model training without sharing private raw data …
[引用][C] AdaCoOpt: Leverage the Interplay of Batch Size and Aggregation Frequency for Federated Learning
W Liu, X Zhang, J Duan, C Joe-Wong, Z Zhou… - 2023 IEEE/ACM 31st …, 2023 - par.nsf.gov