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Privacy-Preserving and Cross-Domain Human Sensing by Federated Domain Adaptation with Semantic Knowledge Correction

Authors:

Wei DongAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 8, Issue 1

Article No.: 6, Pages 1 - 26

https://doi.org/10.1145/3643503

Published: 06 March 2024 Publication History

Abstract

Federated Learning (FL) enables distributed training of human sensing models in a privacy-preserving manner. While promising, federated global models suffer from cross-domain accuracy degradation when the labeled source domains statistically differ from the unlabeled target domain. To tackle this problem, recent methods perform pairwise computation on the source and target domains to minimize the domain discrepancy by adversarial strategy. However, these methods are limited by the fact that pairwise source-target adversarial alignment alone only achieves domain-level alignment, which entails the alignment of domain-invariant as well as environment-dependent features. The misalignment of environment-dependent features may cause negative impact on the performance of the federated global model. In this paper, we introduce FDAS, a Federated adversarial Domain Adaptation with Semantic Knowledge Correction method. FDAS achieves concurrent alignment at both domain and semantic levels to improve the semantic quality of the aligned features, thereby reducing the misalignment of environment-dependent features. Moreover, we design a cross-domain semantic similarity metric and further devise feature selection and feature refinement mechanisms to enhance the two-level alignment. In addition, we propose a similarity-aware model fine-tuning strategy to further improve the target model performance. We evaluate the performance of FDAS extensively on four public and a real-world human sensing datasets. Extensive experiments demonstrate the superior effectiveness of FDAS and its potential in the real-world ubiquitous computing scenarios.

References

[1]

2023. frp. https://github.com/fatedier/frp.

[2]

Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010. A theory of learning from different domains. Machine learning 79 (2010), 151--175.

[3]

Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, and Yue Cheng. 2020. Tifl: A tier-based federated learning system. In Proceedings of the 29th international symposium on high-performance parallel and distributed computing. 125--136.

Digital Library

[4]

Chaoqi Chen, Weiping Xie, Wenbing Huang, Yu Rong, Xinghao Ding, Yue Huang, Tingyang Xu, and Junzhou Huang. 2019. Progressive feature alignment for unsupervised domain adaptation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 627--636.

[5]

Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607.

[6]

Hyunsung Cho, Akhil Mathur, and Fahim Kawsar. 2022. Flame: Federated learning across multi-device environments. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 3 (2022), 1--29.

Digital Library

[7]

Bo Dai, Sanja Fidler, Raquel Urtasun, and Dahua Lin. 2017. Towards diverse and natural image descriptions via a conditional gan. In Proceedings of the IEEE international conference on computer vision. 2970--2979.

[8]

Emily L Denton et al. 2017. Unsupervised learning of disentangled representations from video. Advances in neural information processing systems 30 (2017).

[9]

Chao Feng, Nan Wang, Yicheng Jiang, Xia Zheng, Kang Li, Zheng Wang, and Xiaojiang Chen. 2022. Wi-Learner: Towards One-shot Learning for Cross-Domain Wi-Fi based Gesture Recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 3 (2022), 1--27.

Digital Library

[10]

Haozhe Feng, Zhaoyang You, Minghao Chen, Tianye Zhang, Minfeng Zhu, Fei Wu, Chao Wu, and Wei Chen. 2021. KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation. In ICML. 3274--3283.

[11]

Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016. Domain-adversarial training of neural networks. The journal of machine learning research 17, 1 (2016), 2096--2030.

[12]

Ruiyang Gao, Mi Zhang, Jie Zhang, Yang Li, Enze Yi, Dan Wu, Leye Wang, and Daqing Zhang. 2021. Towards position-independent sensing for gesture recognition with Wi-Fi. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 2 (2021), 1--28.

Digital Library

[13]

Yves Grandvalet and Yoshua Bengio. 2004. Semi-supervised learning by entropy minimization. Advances in neural information processing systems 17 (2004).

[14]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.

[15]

Liang Hou, Qi Cao, Huawei Shen, Siyuan Pan, Xiaoshuang Li, and Xueqi Cheng. 2022. Conditional gans with auxiliary discriminative classifier. In International Conference on Machine Learning. PMLR, 8888--8902.

[16]

Yash Jain, Chi Ian Tang, Chulhong Min, Fahim Kawsar, and Akhil Mathur. 2022. ColloSSL: Collaborative self-supervised learning for human activity recognition. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 1 (2022), 1--28.

Digital Library

[17]

Wenjun Jiang, Chenglin Miao, Fenglong Ma, Shuochao Yao, Yaqing Wang, Ye Yuan, Hongfei Xue, Chen Song, Xin Ma, Dimitrios Koutsonikolas, et al. 2018. Towards environment independent device free human activity recognition. In Proceedings of the 24th annual international conference on mobile computing and networking. 289--304.

Digital Library

[18]

Chenning Li, Xiao Zeng, Mi Zhang, and Zhichao Cao. 2022. PyramidFL: A fine-grained client selection framework for efficient federated learning. In Proceedings of the 28th Annual International Conference on Mobile Computing And Networking. 158--171.

Digital Library

[19]

Jingjing Li, Erpeng Chen, Zhengming Ding, Lei Zhu, Ke Lu, and Zi Huang. 2019. Cycle-consistent conditional adversarial transfer networks. In Proceedings of the 27th ACM international conference on multimedia. 747--755.

Digital Library

[20]

Junnan Li, Pan Zhou, Caiming Xiong, and Steven CH Hoi. 2020. Prototypical contrastive learning of unsupervised representations. arXiv preprint arXiv:2005.04966 (2020).

[21]

Wei-Hong Li, Xialei Liu, and Hakan Bilen. 2022. Cross-domain few-shot learning with task-specific adapters. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7161--7170.

[22]

Xingyu Li, Zhe Qu, Bo Tang, and Zhuo Lu. 2021. Stragglers are not disaster: A hybrid federated learning algorithm with delayed gradients. arXiv preprint arXiv:2102.06329 (2021).

[23]

Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David Carlson, and Jianfeng Gao. 2019. Storygan: A sequential conditional gan for story visualization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6329--6338.

[24]

Yadong Li, Dongheng Zhang, Jinbo Chen, Jinwei Wan, Dong Zhang, Yang Hu, Qibin Sun, and Yan Chen. 2022. Towards domain-independent and real-time gesture recognition using mmwave signal. IEEE Transactions on Mobile Computing (2022).

Digital Library

[25]

Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. 2015. Learning transferable features with deep adaptation networks. In International conference on machine learning. PMLR, 97--105.

[26]

Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan. 2018. Conditional adversarial domain adaptation. Advances in neural information processing systems 31 (2018).

[27]

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR, 1273--1282.

[28]

Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein. 2016. Unrolled generative adversarial networks. arXiv preprint arXiv:1611.02163 (2016).

[29]

Umberto Michieli and Mete Ozay. 2021. Prototype guided federated learning of visual feature representations. arXiv preprint arXiv:2105.08982 (2021).

[30]

Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014).

[31]

Saeid Motiian, Marco Piccirilli, Donald A Adjeroh, and Gianfranco Doretto. 2017. Unified deep supervised domain adaptation and generalization. In Proceedings of the IEEE international conference on computer vision. 5715--5725.

[32]

Dinh C Nguyen, Quoc-Viet Pham, Pubudu N Pathirana, Ming Ding, Aruna Seneviratne, Zihuai Lin, Octavia Dobre, and Won-Joo Hwang. 2022. Federated learning for smart healthcare: A survey. ACM Computing Surveys (CSUR) 55, 3 (2022), 1--37.

Digital Library

[33]

Xiaomin Ouyang, Zhiyuan Xie, Jiayu Zhou, Jianwei Huang, and Guoliang Xing. 2021. Clusterfl: a similarity-aware federated learning system for human activity recognition. In Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services. 54--66.

Digital Library

[34]

Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah Goodman, Pushmeet Kohli, Frank Wood, Philip Torr, et al. 2017. Learning disentangled representations with semi-supervised deep generative models. Advances in neural information processing systems 30 (2017).

[35]

Sinno Jialin Pan and Qiang Yang. 2010. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22, 10 (2010), 1345--1359.

Digital Library

[36]

Xingchao Peng, Zijun Huang, Ximeng Sun, and Kate Saenko. 2019. Domain agnostic learning with disentangled representations. In International Conference on Machine Learning. PMLR, 5102--5112.

[37]

Xingchao Peng, Zijun Huang, Yizhe Zhu, and Kate Saenko. 2019. Federated adversarial domain adaptation. arXiv preprint arXiv:1911.02054 (2019).

[38]

Ariadna Quattoni, Michael Collins, and Trevor Darrell. 2008. Transfer learning for image classification with sparse prototype representations. In 2008 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1--8.

[39]

Konstantin Sozinov, Vladimir Vlassov, and Sarunas Girdzijauskas. 2018. Human activity recognition using federated learning. In 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom). IEEE, 1103--1111.

[40]

Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, and Chengqi Zhang. 2021. Fedproto: Federated prototype learning over heterogeneous devices. arXiv preprint arXiv:2105.00243 (2021).

[41]

Yue Tan, Guodong Long, Jie Ma, Lu Liu, Tianyi Zhou, and Jing Jiang. 2022. Federated learning from pre-trained models: A contrastive learning approach. arXiv preprint arXiv:2209.10083 (2022).

[42]

Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, and Jiashi Feng. 2019. Panet: Few-shot image semantic segmentation with prototype alignment. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 9197--9206.

[43]

Ke Wang, Jiayong Liu, and Jing-Yan Wang. 2019. Learning domain-independent deep representations by mutual information minimization. Computational Intelligence and Neuroscience 2019 (2019).

[44]

Zhengjie Wang, Zehua Huang, Chengming Zhang, Wenwen Dou, Yinjing Guo, and Da Chen. 2021. CSI-based human sensing using model-based approaches: a survey. Journal of Computational Design and Engineering 8, 2 (2021), 510--523.

[45]

Xiaofu Wu, Suofei Zhang, Quan Zhou, Zhen Yang, Chunming Zhao, and Longin Jan Latecki. 2021. Entropy minimization versus diversity maximization for domain adaptation. IEEE Transactions on Neural Networks and Learning Systems (2021).

[46]

Minghao Xu, Hang Wang, Bingbing Ni, Qi Tian, and Wenjun Zhang. 2020. Cross-domain detection via graph-induced prototype alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12355--12364.

[47]

Hong-Ming Yang, Xu-Yao Zhang, Fei Yin, and Cheng-Lin Liu. 2018. Robust classification with convolutional prototype learning. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3474--3482.

[48]

Jianfei Yang, Xinyan Chen, Dazhuo Wang, Han Zou, Chris Xiaoxuan Lu, Sumei Sun, and Lihua Xie. 2022. Deep learning and its applications to WiFi human sensing: A benchmark and a tutorial. arXiv preprint arXiv:2207.07859 (2022).

[49]

Mang Ye, Xu Zhang, Pong C Yuen, and Shih-Fu Chang. 2019. Unsupervised embedding learning via invariant and spreading instance feature. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6210--6219.

[50]

Xu Yu, Dingjia Zhan, Lei Liu, Hongwu Lv, Lingwei Xu, and Junwei Du. 2021. A privacy-preserving cross-domain healthcare wearables recommendation algorithm based on domain-dependent and domain-independent feature fusion. IEEE Journal of Biomedical and Health Informatics 26, 5 (2021), 1928--1936.

[51]

Jie Zhang, Zhanyong Tang, Meng Li, Dingyi Fang, Petteri Nurmi, and Zheng Wang. 2018. CrossSense: Towards cross-site and large-scale WiFi sensing. In Proceedings of the 24th annual international conference on mobile computing and networking. 305--320.

Digital Library

[52]

Xie Zhang, Chengpei Tang, Kang Yin, and Qingqian Ni. 2021. WiFi-Based Cross-Domain Gesture Recognition via Modified Prototypical Networks. IEEE Internet of Things Journal 9, 11 (2021), 8584--8596.

[53]

Yi Zhang, Yue Zheng, Kun Qian, Guidong Zhang, Yunhao Liu, Chenshu Wu, and Zheng Yang. 2021. Widar3. 0: Zero-effort cross-domain gesture recognition with wi-fi. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021).

Cited By

Zhang WLi YAn LWan BWang X(2024)SARS: A Personalized Federated Learning Framework Towards Fairness and Robustness against Backdoor AttacksProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785718:4(1-24)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3678571

Index Terms

Privacy-Preserving and Cross-Domain Human Sensing by Federated Domain Adaptation with Semantic Knowledge Correction
1. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 8, Issue 1

March 2024

1182 pages

EISSN:2474-9567

DOI:10.1145/3651875

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Copyright © 2024 ACM.

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Publication History

Published: 06 March 2024

Published in IMWUT Volume 8, Issue 1

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Cited By

Zhang WLi YAn LWan BWang X(2024)SARS: A Personalized Federated Learning Framework Towards Fairness and Robustness against Backdoor AttacksProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785718:4(1-24)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3678571

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