Local Adaptive Federated Learning with Channel Personalized Normalization

ZHAO Yu; CHEN Siguang

doi:10.11999/JEIT231165

Volume 46 Issue 8

Aug. 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(8): 3174-3183

ZHAO Yu, CHEN Siguang. Local Adaptive Federated Learning with Channel Personalized Normalization[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3174-3183. doi: 10.11999/JEIT231165

Citation:

ZHAO Yu, CHEN Siguang. Local Adaptive Federated Learning with Channel Personalized Normalization[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3174-3183. doi: 10.11999/JEIT231165

ZHAO Yu, CHEN Siguang. Local Adaptive Federated Learning with Channel Personalized Normalization[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3174-3183. doi: 10.11999/JEIT231165

Citation:

PDF( 4206 KB)

Local Adaptive Federated Learning with Channel Personalized Normalization

doi: 10.11999/JEIT231165 cstr: 32379.14.JEIT231165

ZHAO Yu,
CHEN Siguang^,

School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Funds: The National Natural Science Foundation of China (61971235), The 333 High-level Talents Training Project of Jiangsu Province, and the 1311 Talents Plan of NJUPT

Received Date: 2023-10-26
Rev Recd Date: 2024-01-24

Available Online: 2024-03-04

Publish Date: 2024-08-30

Abstract

Abstract

To relieve the impact of data heterogeneity problems caused by full overlapping attribute skew between clients in Federated Learning (FL), a local adaptive FL algorithm that incorporates channel personalized normalization is proposed in this paper. Specifically, an FL model oriented to data attribute skew is constructed, and a series of random enhancement operations are performed on the images data set in the client before training begins. Next, the client calculates the mean and standard deviation of the data set separately by color channel to achieve channel personalized normalization. Furthermore, a local adaptive update FL algorithm is designed, that is, the global model and the local model are adaptively aggregated for local initialization. The uniqueness of this aggregation method is that it not only retains the personalized characteristics of the client model, but also can capture necessary information in the global model to improve the generalization performance of the model. Finally, the experimental results demonstrate that the proposed algorithm obtains competitive convergence speed compared with existing representative works and the accuracy is 3%～19% higher.
- Edge computing,
- Federated Learning (FL),
- Normalization,
- Model aggregation

FullText(HTML)

References(27)

References

[1]	KAISSIS G A, MAKOWSKI M R, RÜCKERT D, et al. Secure, privacy-preserving and federated machine learning in medical imaging[J]. Nature Machine Intelligence, 2020, 2(6): 305–311. doi: 10.1038/s42256-020-0186-1.
[2]	TANG Zhenheng, SHI Shaohuai, and CHU Xiaowen. Communication-efficient decentralized learning with sparsification and adaptive peer selection[C]. IEEE 40th International Conference on Distributed Computing Systems (ICDCS), Singapore, 2020: 1207–1208. doi: 10.1109/ICDCS47774.2020.00153.
[3]	YANG Qiang, LIU Yang, CHEN Tianjian, et al. Federated machine learning: Concept and applications[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2): 12. doi: 10.1145/3298981.
[4]	CHEN Yang, SUN Xiaoyan, and JIN Yaochu. Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(10): 4229–4238. doi: 10.1109/TNNLS.2019.2953131.
[5]	WU Donglei, ZOU Xiangyu, ZHANG Shuyu, et al. SmartIdx: Reducing communication cost in federated learning by exploiting the CNNs structures[C]. The 36th AAAI Conference on Artificial Intelligence, 2022: 4254–4262. doi: 10.1609/aaai.v36i4.20345.
[6]	MILLS J, HU Jia, and MIN Geyong. Communication-efficient federated learning for wireless edge intelligence in IoT[J]. IEEE Internet of Things Journal, 2020, 7(7): 5986–5994. doi: 10.1109/JIOT.2019.2956615.
[7]	SATTLER F, WIEDEMANN S, MÜLLER K R, et al. Robust and communication-efficient federated learning from non-I. I. D. data[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(9): 3400–3413. doi: 10.1109/TNNLS.2019.2944481.
[8]	DUAN Moming, LIU Duo, CHEN Xianzhang, et al. Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications[C]. IEEE 37th International Conference on Computer Design (ICCD), Abu Dhabi, United Arab Emirates, 2019: 246–254. doi: 10.1109/ICCD46524.2019.00038.
[9]	LIM W Y B, LUONG N C, HOANG D T, et al. Federated learning in mobile edge networks: A comprehensive survey[J]. IEEE Communications Surveys & Tutorials, 2020, 22(3): 2031–2063. doi: 10.1109/COMST.2020.2986024.
[10]	MENDIETA M, YANG Taojiannan, WANG Pu, et al. Local learning matters: Rethinking data heterogeneity in federated learning[C]. The 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, USA, 2022: 8387–8396. doi: 10.1109/CVPR52688.2022.00821.
[11]	WANG Jianyu, LIU Qinghua, LIANG Hao, et al. Tackling the objective inconsistency problem in heterogeneous federated optimization[C]. The 34th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2020: 638. doi: 10.48550/arXiv.2007.07481.
[12]	LI Qinbin, HE Bingsheng, and SONG D. Model-contrastive federated learning[C]. The 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA, 2021: 10708–10717. doi: 10.1109/CVPR46437.2021.01057.
[13]	GHOSH A, CHUNG J, YIN Dong, et al. An efficient framework for clustered federated learning[J]. IEEE Transactions on Information Theory, 2022, 68(12): 8076–8091. doi: 10.1109/TIT.2022.3192506.
[14]	SATTLER F, MÜLLER K R, and SAMEK W. Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(8): 3710–3722. doi: 10.1109/TNNLS.2020.3015958.
[15]	KULKARNI V, KULKARNI M, and PANT A. Survey of personalization techniques for federated learning[C]. The 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), London, UK, 2020: 794–797. doi: 10.1109/WorldS450073.2020.9210355.
[16]	MCMAHAN B, MOORE E, RAMAGE D, et al. Communication-efficient learning of deep networks from decentralized data[C]. The 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, USA, 2017: 1273–1282.
[17]	ZHAO Zhongyuan, FENG Chenyuan, HONG Wei, et al. Federated learning with non-iid data in wireless networks[J]. IEEE Transactions on Wireless Communications, 2021, 21(3): 1927–1942. doi: 10.1109/TWC.2021.3108197.
[18]	KE Guolin, MENG Qi, FINLEY T, et al. LightGBM: A highly efficient gradient boosting decision tree[C]. The 31st International Conference on Neural Information Processing Systems, Long Beach, USA, 2017: 3149–3157.
[19]	LI Qinbin, DIAO Yiqun, CHEN Quan, et al. Federated learning on non-IID data silos: An experimental study[C]. 2022 IEEE 38th International Conference on Data Engineering, Kuala Lumpur, Malaysia, 2022: 965–978. doi: 10.1109/ICDE53745.2022.00077.
[20]	LI Tian, SAHU A K, ZAHEER M, et al. Federated optimization in heterogeneous networks[C]. Machine Learning and Systems, Austin, USA, 2020: 1–22. doi: 10.48550/arXiv.1812.06127.
[21]	WANG Hao, KAPLAN Z, NIU Di, et al. Optimizing federated learning on non-iid data with reinforcement learning[C]. The IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, Toronto, Canada, 2020: 1698–1707. doi: 10.1109/INFOCOM41043.2020.9155494.
[22]	WANG Hongyi, YUROCHKIN M, SUN Yuekai, et al. Federated learning with matched averaging[C]. The 8th International Conference on Learning Representations, Addis Ababa, Ethiopia, 2020: 1–16.
[23]	HUANG Yutao, CHU Lingyang, ZHOU Zirui, et al. Personalized cross-silo federated learning on non-IID data[C]. The 35th AAAI Conference on Artificial Intelligence, 2021: 7865–7873. doi: 10.1609/aaai.v35i9.16960.
[24]	LI Xinchun, ZHAN Dechuan, SHAO Yunfeng, et al. FedPHP: Federated personalization with inherited private models[C]. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Bilbao, Spain, 2021: 587–602. doi: 10.1007/978-3-030-86486-6_36.
[25]	ZHANG M, SAPRA K, FIDLER S, et al. Personalized federated learning with first order model optimization[C]. The 9th International Conference on Learning Representations (ICLR), 2021: 1–17.
[26]	LUO Jun and WU Shandong. Adapt to adaptation: Learning personalization for cross-silo federated learning[C]. 31st International Joint Conference on Artificial Intelligence, Vienna, Austria, 2022: 2166–2173.
[27]	LI Xiaoxiao, JIANG Meirui, ZHANG Xiaofei, et al. FedBN: Federated learning on non-IID features via local batch normalization[C]. The 9th International Conference on Learning Representations, 2021: 1–27.