TY - GEN
T1 - FedGPO
T2 - 2022 IEEE International Symposium on Workload Characterization, IISWC 2022
AU - Kim, Young Geun
AU - Wu, Carole Jean
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Federated learning (FL) has emerged as a solution to deal with the risk of privacy leaks in machine learning training. This approach allows a variety of mobile devices to collaboratively train a machine learning model without sharing the raw on-device training data with the cloud. However, efficient edge deployment of FL is challenging because of the system/data heterogeneity and runtime variance. This paper optimizes the energy-efficiency of FL use cases while guaranteeing model convergence, by accounting for the aforementioned challenges. We propose FedGPO based on a reinforcement learning, which learns how to identify optimal global parameters (B, E, K) for each FL aggregation round adapting to the system/data heterogeneity and stochastic runtime variance. In our experiments, FedGPO improves the model convergence time by 2.4 times, and achieves 3.6 times higher energy efficiency over the baseline settings, respectively.
AB - Federated learning (FL) has emerged as a solution to deal with the risk of privacy leaks in machine learning training. This approach allows a variety of mobile devices to collaboratively train a machine learning model without sharing the raw on-device training data with the cloud. However, efficient edge deployment of FL is challenging because of the system/data heterogeneity and runtime variance. This paper optimizes the energy-efficiency of FL use cases while guaranteeing model convergence, by accounting for the aforementioned challenges. We propose FedGPO based on a reinforcement learning, which learns how to identify optimal global parameters (B, E, K) for each FL aggregation round adapting to the system/data heterogeneity and stochastic runtime variance. In our experiments, FedGPO improves the model convergence time by 2.4 times, and achieves 3.6 times higher energy efficiency over the baseline settings, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85145658581&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145658581&partnerID=8YFLogxK
U2 - 10.1109/IISWC55918.2022.00020
DO - 10.1109/IISWC55918.2022.00020
M3 - Conference contribution
AN - SCOPUS:85145658581
T3 - Proceedings - 2022 IEEE International Symposium on Workload Characterization, IISWC 2022
SP - 117
EP - 129
BT - Proceedings - 2022 IEEE International Symposium on Workload Characterization, IISWC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 November 2022 through 8 November 2022
ER -