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Multimodal Federated Learning (MMFL) utilizes multiple modalities in each client to build a more powerful Federated Learning (FL) model than its unimodal counterpart.
Multi-view regression via canonical correlation analysis
Kakade, S. M. and Foster, D. P · 2007
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Deep canonical correlation analysis
Andrew, G., Arora, R., Bilmes, J., and Livescu, K · 2013
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Preparing a collection of radiology examinations for distribution and retrieval
Demner-Fushman, D., Kohli, M. D., Rosenman, M. B., Shooshan, S. E., Rodriguez, L., Antani, S., Thoma, G. R., and McDonald, C. J · 2016
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Convolutional mkl based multimodal emotion recognition and sentiment analysis
Poria, S., Chaturvedi, I., Cambria, E., and Hussain, A · 2016
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Multi-rate deep learning for temporal recommendation
Song, Y., Elkahky, A. M., and He, X · 2016
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Multimodal autoencoder: A deep learning approach to filling in missing sensor data and enabling better mood prediction
Jaques, N., Taylor, S., Sano, A., and Picard, R · 2017
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Tensor fusion network for multimodal sentiment analysis
Zadeh, A., Chen, M., Poria, S., Cambria, E., and Morency, L.-P · 2017
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Deep adversarial learning for multi-modality missing data completion
Cai, L., Wang, Z., Gao, H., Shen, D., and Ji, S · 2018
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Semi-supervised deep generative modelling of incomplete multi-modality emotional data
Du, C., Du, C., Wang, H., Li, J., Zheng, W.-L., Lu, B.-L., and He, H · 2018
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Video generation from text
Li, Y., Min, M., Shen, D., Carlson, D., and Carin, L · 2018
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Efficient low-rank multimodal fusion with modality-specific factors
Liu, Z., Shen, Y., Lakshminarasimhan, V. B., Liang, P. P., Zadeh, A., and Morency, L.-P · 2018
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Toward marker-free 3d pose estimation in lifting: A deep multi-view solution
Mehrizi, R., Peng, X., Tang, Z., Xu, X., Metaxas, D., and Li, K · 2018
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Multimodal and multi-view models for emotion recognition
Aguilar, G., Rozgić, V., Wang, W., and Wang, C · 2019
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Implicit fusion by joint audiovisual training for emotion recognition in mono modality
Han, J., Zhang, Z., Ren, Z., and Schuller, B · 2019
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Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports
Johnson, A. E., Pollard, T. J., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-y., Mark, R. G., and Horng, S · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Kenton, J. D. M.-W. C. and Toutanova, L. K · 2019
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Found in translation: Learning robust joint representations by cyclic translations between modalities
Pham, H., Liang, P. P., Manzini, T., Morency, L.-P., and Póczos, B · 2019
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Metric learning on healthcare data with incomplete modalities
Suo, Q., Zhong, W., Ma, F., Yuan, Y., Gao, J., and Zhang, A · 2019
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Federated learning: A survey on enabling technologies, protocols, and applications
Aledhari, M., Razzak, R., Parizi, R. M., and Saeed, F · 2020
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On the benefits of early fusion in multimodal representation learning
Barnum, G., Talukder, S. J., and Yue, Y · 2020
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Fedbe: Making bayesian model ensemble applicable to federated learning
Chen, H.-Y. and Chao, W.-L · 2020
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Rethinking attention with performers
Choromanski, K. M., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J. Q., Mohiuddin, A., Kaiser, L., et al · 2020
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Federated optimization in heterogeneous networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2020
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Fedmsplit: Correlation-adaptive federated multi-task learning across multimodal split networks
Chen, J. and Zhang, A · 2022
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Towards optimal multi-modal federated learning on non-iid data with hierarchical gradient blending
Chen, S. and Li, B · 2022
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Learn from others and be yourself in heterogeneous federated learning
Huang, W., Ye, M., and Du, B · 2022
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Multi-modal understanding and generation for medical images and text via vision-language pre-training
Moon, J. H., Lee, H., Shin, W., Kim, Y.-H., and Choi, E · 2022
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A federated learning method for real-time emotion state classification from multi-modal streaming
Nandi, A. and Xhafa, F · 2022
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Lin, T., Kong, L., Stich, S. U., and Jaggi, M · 2020
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Federated learning for vision-and-language grounding problems
Liu, F., Wu, X., Ge, S., Fan, W., and Zou, Y · 2020
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Training strategies to handle missing modalities for audio-visual expression recognition
Parthasarathy, S. and Sundaram, S · 2020
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Transmodality: An end2end fusion method with transformer for multimodal sentiment analysis
Wang, Z., Wan, Z., and Wan, X · 2020
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Federated learning based on dynamic regularization
Acar, D. A. E., Zhao, Y., Navarro, R. M., Mattina, M., Whatmough, P. N., and Saligrama, V · 2021
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Multimodal melanoma detection with federated learning
Agbley, B. L. Y., Li, J., Haq, A. U., Bankas, E. K., Ahmad, S., Agyemang, I. O., Kulevome, D., Ndiaye, W. D., Cobbinah, B., and Latipova, S · 2021
Cited alongside, same era.
A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets
Bayoudh, K., Knani, R., Hamdaoui, F., and Mtibaa, A · 2021
Cited alongside, same era.
Qayyum, A., Ahmad, K., Ahsan, M. A., Al-Fuqaha, A., and Qadir, J · 2022
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Flash: Federated learning for automated selection of high-band mmwave sectors
Salehi, B., Gu, J., Roy, D., and Chowdhury, K · 2022
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A unified framework for multi-modal federated learning
Xiong, B., Yang, X., Qi, F., and Xu, C · 2022
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Multimodal federated learning via contrastive representation ensemble
Yu, Q., Liu, Y., Wang, Y., Xu, K., and Liu, J · 2022
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Multimodal federated learning on iot data
Zhao, Y., Barnaghi, P., and Haddadi, H · 2022
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pfedprompt: Learning personalized prompt for vision-language models in federated learning
Guo, T., Guo, S., and Wang, J · 2023
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Fedmekt: Distillation-based embedding knowledge transfer for multimodal federated learning
Le, H. Q., Nguyen, M. N., Thwal, C. M., Qiao, Y., Zhang, C., and Hong, C. S · 2023
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Unified chest x-ray and radiology report generation model with multi-view chest x-rays
Lee, H., Kim, W., Kim, J.-H., Kim, T., Kim, J., Sunwoo, L., and Choi, E · 2023
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Rethinking semi-supervised federated learning: How to co-train fully-labeled and fully-unlabeled client imaging data
Saha, P., Mishra, D., and Noble, J. A · 2023
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Feddm: Iterative distribution matching for communication-efficient federated learning
Xiong, Y., Wang, R., Cheng, M., Yu, F., and Hsieh, C.-J · 2023
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Multimodal learning with transformers: A survey
Xu, P., Zhu, X., and Clifton, D. A · 2023
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Multimodal federated learning via contrastive representation ensemble
Yu, Q., Liu, Y., Wang, Y., Xu, K., and Liu, J · 2023
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