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240222s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2024.3367412
|2 doi
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| 028 |
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|a pubmed24n1459.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Yang, Xiaoshan
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Cross-Modal Federated Human Activity Recognition
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| 264 |
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|c 2024
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| 336 |
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 02.07.2024
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|a Date Revised 02.07.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Federated human activity recognition (FHAR) has attracted much attention due to its great potential in privacy protection. Existing FHAR methods can collaboratively learn a global activity recognition model based on unimodal or multimodal data distributed on different local clients. However, it is still questionable whether existing methods can work well in a more common scenario where local data are from different modalities, e.g., some local clients may provide motion signals while others can only provide visual data. In this article, we study a new problem of cross-modal federated human activity recognition (CM-FHAR), which is conducive to promote the large-scale use of the HAR model on more local devices. CM-FHAR has at least three dedicated challenges: 1) distributive common cross-modal feature learning, 2) modality-dependent discriminate feature learning, 3) modality imbalance issue. To address these challenges, we propose a modality-collaborative activity recognition network (MCARN), which can comprehensively learn a global activity classifier shared across all clients and multiple modality-dependent private activity classifiers. To produce modality-agnostic and modality-specific features, we learn an altruistic encoder and an egocentric encoder under the constraint of a separation loss and an adversarial modality discriminator collaboratively learned in hyper-sphere. To address the modality imbalance issue, we propose an angular margin adjustment scheme to improve the modality discriminator on modality-imbalanced data by enhancing the intra-modality compactness of the dominant modality and increase the inter-modality discrepancy. Moreover, we propose a relation-aware global-local calibration mechanism to constrain class-level pairwise relationships for the parameters of the private classifier. Finally, through decentralized optimization with alternative steps of adversarial local updating and modality-aware global aggregation, the proposed MCARN obtains state-of-the-art performance on both modality-balanced and modality-imbalanced data
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Xiong, Baochen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Huang, Yi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Changsheng
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 46(2024), 8 vom: 19. Juli, Seite 5345-5361
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:46
|g year:2024
|g number:8
|g day:19
|g month:07
|g pages:5345-5361
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| 856 |
4 |
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|u http://dx.doi.org/10.1109/TPAMI.2024.3367412
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|d 46
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