|
Bibliography
[1] Multimodal sensor market in 2022 : Detailed study on business strate- gies, development factors, future trends, opportunities, and demand out- look till 2028 with fastest growing regions and countries data, 2022. https://southeast.newschannelnebraska.com/story/46208517/Multimodal-Sensor- Market.
[2] D. Acar, Y. Zhao, R. Matas, M. Mattina, P. Whatmough, and V. Saligrama. Fed- erated learning based on dynamic regularization. In Proc. of International Confer- ence on Learning Representations (ICLR), pages 1–43, Los Angeles, USA, May 2020.
[3] M. Aledhari, R. Razzak, R. Parizi, and F. Saeed. Federated learning: A survey on enabling technologies, protocols, and applications. IEEE Access, 8, 2020.
[4] P. Antoniadis, I. Pikoulis, P. Filntisis, and P. Maragos. An audiovisual and contex- tual approach for categorical and continuous emotion recognition in-the-wild. In Proc. of IEEE/CVF International Conference on Computer Vision (ICCV), pages 3645–3651, Virtual, 2021.
[5] I. Ariav and I. Cohen. An end-to-end multimodal voice activity detection using wavenet encoder and residual networks. IEEE Journal of Selected Topics in Signal Processing, 13(2):265–274, 2019.
[6] E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov. How to backdoor federated learning. In Proc. of International Conference on Artificial Intelligence and Statistics (AISTATS), pages 2938–2948, Virtual, 2020.
[7] T. Baltrusaitis, C. Ahuja, and L.-P. Morency. Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intel- ligence, 41(2), 2018.
[8] H. B. Barlow. Unsupervised learning. Neural Computation, 1(3):295–311, 1989.
[9] P. Bellavista, L. Foschini, and A. Mora. Decentralised learning in federated de- ployment environments: A system-level survey. ACM Computing Surveys, 54(1), 2021. [10] N. Burkart and M. Huber. A survey on the explainability of supervised machine learning. Journal of Artificial Intelligence Research, 70:245–317, 2021. [11] C. Busso, M. Bulut, C.-C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J. Chang, S. Lee, and S. Narayanan. IEMOCAP: Interactive emotional dyadic motion capture database. Language Resources and Evaluation, 42(4), 2008. [12] M. Chen, D. Gunduz, K. Huang, W. Saad, M. Bennis, A. V. Feljan, and H. V. Poor. Distributed learning in wireless networks: Recent progress and future challenges. IEEE Journal on Selected Areas in Communications, 2021. [13] M. Chen, N. Shlezinger, H. Poor, Y. Eldar, and S. Cui. Communication-efficient federated learning. Proc. of National Academy of Sciences, 118(17), 2021. [14] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton. A simple framework for con- trastive learning of visual representations. In Proc. of International Conference on Machine Learning (ICML), pages 1597–1607, Virtual, June 2020. [15] Y. Chen, X. Qin, J. Wang, C. Yu, and W. Gao. Fedhealth: A federated transfer learning framework for wearable healthcare. IEEE Intelligent Systems, 35(4):83– 93, 2020. [16] Y. Chen, X. Sun, and Y. Jin. Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation. IEEE Transactions on Neural Networks and Learning Systems, 31(10), 2019. [17] Z. Chen, L. Zhang, Z. Cao, and J. Guo. Distilling the knowledge from handcrafted features for human activity recognition. IEEE Transactions on Industrial Informat- ics, 14(10):4334–4342, 2018. [18] M. Cheng, X. Jiao, Y. Liu, M. Shao, X. Yu, Y. Bai, Z. Wang, S. Wang, N. Tuohuti, S. Liu, et al. Estimation of soil moisture content under high maize canopy coverage from uav multimodal data and machine learning. Agricultural Water Management, 264:107530, 2022. [19] Y. Cheng, D. Wang, P. Zhou, and T. Zhang. Model compression and acceleration for deep neural networks: The principles, progress, and challenges. IEEE Signal Processing Magazine, 35(1), 2018.
[20] P. Cunningham, M. Cord, and S. Delany. Supervised learning. In Machine Learn- ing Techniques for Multimedia, pages 21–49. 2008. [21] B. Darwin, P. Dharmaraj, S. Prince, D. Popescu, and D. Hemanth. Recognition of bloom/yield in crop images using deep learning models for smart agriculture: A review. Agronomy, 11(4), 2021. [22] V. De Silva, J. Roche, and A. Kondoz. Robust fusion of LiDAR and wide-angle camera data for autonomous mobile robots. Sensors, 18(8):2730, 2018. [23] F. Demrozi, G. Pravadelli, A. Bihorac, and P. Rashidi. Human activity recognition using inertial, physiological and environmental sensors: A comprehensive survey. IEEE Access, 8:210816–210836, 2020. [24] C. Dinh, N. Tran, and T. Nguyen. Personalized federated learning with moreau envelopes. Advances in Neural Information Processing Systems, 33:21394–21405, 2020. [25] H. Du, P. Henry, X. Ren, M. Cheng, D. Goldman, S. Seitz, and D. Fox. Interactive 3D modeling of indoor environments with a consumer depth camera. In Proc. of International Conference on Ubiquitous Computing (UBIC), pages 75–84, Ankara, Turkey, 2011. [26] C. Dwork, A. Roth, et al. The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci., 9(3-4):211–407, 2014.
[27] A. M. Elbir, S. Coleri, and K. V. Mishra. Hybrid federated and centralized learn- ing. In Proc. of European Signal Processing Conference (EUSIPCO), pages 1541– 1545, Dublin, Ireland, 2021. [28] Z. Erickson, E. Xing, B. Srirangam, S. Chernova, and C. Kemp. Multimodal mate- rial classification for robots using spectroscopy and high resolution texture imag- ing. In Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10452–10459, Las Vegas, USA, 2020. [29] A. Fallah, A. Mokhtari, and A. Ozdaglar. Personalized federated learning: A meta- learning approach. In Proc. of International Conference on Neural Information Processing Systems (NeurIPS), December 2020. [30] V. Ganchenko and A. Doudkin. Image semantic segmentation based on convolu- tional neural networks for monitoring agricultural vegetation. In Proc. of Inter- national Conference on Pattern Recognition and Information Processing (PRIP), pages 52–63, Minsk, Belarus, 2019.
[31] J. Gao, P. Li, Z. Chen, and J. Zhang. A survey on deep learning for multimodal data fusion. Neural Computation, 32(5):829–864, 2020.
[32] A. Garcia-Garcia, S. Orts-Escolano, S. Oprea, V. Villena-Martinez, P. Martinez- Gonzalez, and J. Garcia-Rodriguez. A survey on deep learning techniques for image and video semantic segmentation. Elsevier Applied Soft Computing, 70, 2018.
[33] C. Gentry. A fully homomorphic encryption scheme. Stanford university, 2009.
[34] N. Gholizadeh and P. Musilek. Distributed learning applications in power systems: A review of methods, gaps, and challenges. Energies, 14(12):3654, 2021.
[35] R. Gilad-Bachrach, N. Dowlin, K. Laine, K. Lauter, M. Naehrig, and J. Werns- ing. Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In Proc. of International Conference on Machine Learning (ICML), pages 201–210, New York, USA, 2016.
[36] J. Gou, B. Yu, S. Maybank, and D. Tao. Knowledge distillation: A survey. Springer International Journal of Computer Vision, 129(6), 2021.
[37] N. Guha, A. Talwalkar, and V. Smith. One-shot federated learning. arXiv preprint arXiv:1902.11175, 2019.
[38] E. Gumuslu, D. Barkana, and H. Kose. Emotion recognition using EEG and phys- iological data for robot-assisted rehabilitation systems. In Proc. of International Conference on Multimodal Interaction (ICMI), pages 379–387, Utrecht, Nether- lands, 2020.
[39] W. Guo, J. Wang, and S. Wang. Deep multimodal representation learning: A sur- vey. IEEE Access, 7, 2019.
[40] A. Gupta, A. Anpalagan, L. Guan, and A. Khwaja. Deep learning for object detec- tion and scene perception in self-driving cars: Survey, challenges, and open issues. Array, 10:100057, 2021.
[41] Q. Ha, K. Watanabe, T. Karasawa, Y. Ushiku, and T. Harada. MFNet: Towards real- time semantic segmentation for autonomous vehicles with multi-spectral scenes. In Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5108–5115, British Columbia, Canada, 2017.
[42] L. Hao, N. Rohani, R. Zhao, E. Pulver, H. Mak, O. Kelada, H. Ko, H. Fleming, F. Gertler, and S. Bhatia. Microenvironment-triggered multimodal precision diag- nostics. Nature Materials, 20(10):1440–1448, 2021. [43] A. Hard, K. Rao, R. Mathews, S. Ramaswamy, F. Beaufays, S. Augenstein, H. Eichner, C. Kiddon, and D. Ramage. Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604, 2018. [44] G. Hinton, O. Vinyals, and J. Dean. Distilling the knowledge in a neural network. In Proc. of NIPS Deep Learning and Representation Learning Workshop (NeurIPS), December 2015. [45] W. Hong, X. Luo, Z. Zhao, M. Peng, and T. Quek. Optimal design of hybrid federated and centralized learning in the mobile edge computing systems. In Proc. of IEEE International Conference on Communications Workshops (ICC), pages 1– 6, Virtual, 2021. [46] F. Huang, X. Zhang, Z. Zhao, J. Xu, and Z. Li. Image–text sentiment analysis via deep multimodal attentive fusion. Knowledge-Based Systems, 167:26–37, 2019. [47] L. Huang, Y. Yin, Z. Fu, S. Zhang, H. Deng, and D. Liu. LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on iid and non-iid intensive care data. Plos One, 15(4), 2020. [48] L.-R. Jacome-Galarza. Crop yield prediction utilizing multimodal deep learning. In Proc. of Iberian Conference on Information Systems and Technologies (CISTI), pages 1–6, Chaves, Portugal, 2021. [49] C. Janiesch, P. Zschech, and K. Heinrich. Machine learning and deep learning. Electronic Markets, 31(3):685–695, 2021.
[50] E. Jeong, S. Oh, H. Kim, J. Park, M. Bennis, and S.-L. Kim. Communication- efficient on-device machine learning: Federated distillation and augmentation un- der non-iid private data. In Proc. of International Conference on Neural Informa- tion Processing Systems (NeurIPS), December 2018. [51] E. Jeong, S. Oh, J. Park, H. Kim, M. Bennis, and S.-L. Kim. Hiding in the crowd: Federated data augmentation for on-device learning. IEEE Intelligent Systems, 36(5), 2020. [52] H. Jiang and Y. Guo. Multi-class multimodal semantic segmentation with an im- proved 3D fully convolutional networks. Neurocomputing, 391:220–226, 2020.
[53] M. Jordan and T. Mitchell. Machine learning: Trends, perspectives, and prospects. Science, 349(6245):255–260, 2015.
[54] L. Kaelbling, M. Littman, and A. Moore. Reinforcement learning: A survey. Jour- nal of Artificial Intelligence Research, 4:237–285, 1996.
[55] P. Kairouz, H. McMahan, B. Avent, A. Bellet, et al. Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2):1– 210, 2021.
[56] D. Kothandaraman, A. Nambiar, and A. Mittal. Domain adaptive knowledge dis- tillation for driving scene semantic segmentation. In Proc. of IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 134–143, Virtual, 2021.
[57] A. Krizhevsky, G. Hinton, et al. Learning multiple layers of features from tiny images. 2009.
[58] V. Kulkarni, M. Kulkarni, and A. Pant. Survey of personalization techniques for federated learning. In Proc. of Fourth World Conference on Smart Trends in Sys- tems, Security and Sustainability (WorldS4), pages 794–797, Virtual, 2020.
[59] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proc. of IEEE, 86(11):2278–2324, 1998.
[60] J. Lee, S. Kim, S. Kim, J. Park, and K. Sohn. Context-aware emotion recognition networks. In Proc. of IEEE/CVF International Conference on Computer Vision (ICCV), pages 10143–10152, Seoul, Korea, 2019.
[61] D. Li and J. Wang. Fedmd: Heterogenous federated learning via model distillation. arXiv preprint arXiv:1910.03581, 2019.
[62] H. Li, A. Shrestha, H. Heidari, J. Le Kernec, and F. Fioranelli. Bi-lstm network for multimodal continuous human activity recognition and fall detection. IEEE Sensors Journal, 20(3):1191–1201, 2019.
[63] M. Li, D. Andersen, J. Park, A. Smola, A. Ahmed, V. Josifovski, J. Long, E. Shekita, and B.-Y. Su. Scaling distributed machine learning with the param- eter server. In Proc. of USENIX Symposium on Operating Systems Design and Implementation (OSDI), pages 583–598, Broomfield, USA, 2014.
[64] Q. Li, B. He, and D. Song. Model-contrastive federated learning. In Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10713–10722, Virtual, 2021. [65] T. Li, A. Sahu, A. Talwalkar, and V. Smith. Federated learning: challenges, meth- ods, and future directions. IEEE Signal Processing Magazine, 37(3), 2020. [66] T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith. Federated optimization in heterogeneous networks. In Proc. of Conference on Systems and Machine Learning (SysML), 2:429–450, 2018. [67] X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang. On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189, 2019. [68] X. Li, M. Jiang, X. Zhang, M. Kamp, and Q. Dou. FedBN: Federated learning on non-iid features via local batch normalization. In Proc. of International Conference on Learning Representations (ICLR), pages 1–27, Virtual, 2020. [69] Y. Li, H. Qi, J. Dai, X. Ji, and Y. Wei. Fully convolutional instance-aware semantic segmentation. In Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2359–2367, Honolulu, Hawaii, 2017. [70] T. Lin, L. Kong, S. Stich, and M. Jaggi. Ensemble distillation for robust model fusion in federated learning. In Proc. of Conference on Neural Information Pro- cessing Systems (NeurIPS), 2020. [71] Q. Liu, C. Chen, J. Qin, Q. Dou, and P.-A. Heng. FedDG: Federated domain gen- eralization on medical image segmentation via episodic learning in continuous fre- quency space. In Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1013–1023, Virtual, 2021. [72] X. Liu, Y. Han, S. Bai, Y. Ge, T. Wang, X. Han, S. Li, J. You, and J. Lu. Importance- aware semantic segmentation in self-driving with discrete wasserstein training. In Proc. of AAAI Conference on Artificial Intelligence (AAAI), volume 34, pages 11629–11636, Hilton, USA, 2020. [73] Y. Liu, Y. Kang, C. Xing, T. Chen, and Q. Yang. A secure federated transfer learning framework. IEEE Intelligent Systems, 35(4):70–82, 2020. [74] Y. Liu, K. Wang, G. Li, and L. Lin. Semantics-aware adaptive knowledge distilla- tion for sensor-to-vision action recognition. IEEE Transactions on Image Process- ing, 30:5573–5588, 2021.
[75] Z. Liu, Y. Shen, V. Lakshminarasimhan, P. Liang, A. Zadeh, and L.-P. Morency. Efficient low-rank multimodal fusion with modality-specific factors. arXiv preprint arXiv:1806.00064, 2018. [76] Y. Lu, K. Xu, L. Zhang, M. Deguchi, H. Shishido, T. Arie, R. Pan, A. Hayashi, L. Shen, S. Akita, et al. Multimodal plant healthcare flexible sensor system. ACS Nano, 14(9):10966–10975, 2020. [77] M. Luo, F. Chen, D. Hu, Y. Zhang, J. Liang, and J. Feng. No fear of heterogeneity: Classifier calibration for federated learning with non-iid data. Advances in Neural Information Processing Systems, 34, 2021. [78] L. Lyu, H. Yu, and Q. Yang. Threats to federated learning: A survey. arXiv preprint arXiv:2003.02133, 2020. [79] N. Majumder, D. Hazarika, A. Gelbukh, E. Cambria, and S. Poria. Multimodal sentiment analysis using hierarchical fusion with context modeling. Knowledge- Based Systems, 161:124–133, 2018. [80] Y. Matsubara, M. Levorato, and F. Restuccia. Split computing and early exiting for deep learning applications: Survey and research challenges. arXiv preprint arXiv:2103.04505, 2021. [81] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Arcas. Communication- efficient learning of deep networks from decentralized data. In Proc. of Interna- tional Conference on Artificial Intelligence and Statistics (AISTATS), pages 1273– 1282, Lauderdale, 2017. [82] J. Mills, J. Hu, and G. Min. Communication-efficient federated learning for wire- less edge intelligence in IoT. IEEE Internet of Things Journal, 7(7):5986–5994, 2019. [83] G. Muhammad, F. Alshehri, F. Karray, A. El Saddik, M. Alsulaiman, and T. Falk. A comprehensive survey on multimodal medical signals fusion for smart healthcare systems. Information Fusion, 76:355–375, 2021. [84] H. Noh, S. Hong, and B. Han. Learning deconvolution network for semantic seg- mentation. In Proc. of IEEE/CVF International Conference on Computer Vision (ICCV), pages 1520–1528, Santiago, Chile, 2015. [85] B. Nojavanasghari, D. Gopinath, J. Koushik, T. Baltruvsaitis, and L.-P. Morency. Deep multimodal fusion for persuasiveness prediction. In Proc. of ACM Inter-
national Conference on Multimodal Interaction (ICMI), pages 284–288, Tokyo, Japan, 2016. [86] T. Ogawa, Y. Sasaka, K. Maeda, and M. Haseyama. Favorite video classification based on multimodal bidirectional LSTM. IEEE Access, 6:61401–61409, 2018. [87] J. Park, S. Wang, A. Elgabli, S. Oh, E. Jeong, H. Cha, H. Kim, S.-L. Kim, and M. Bennis. Distilling on-device intelligence at the network edge. arXiv preprint arXiv:1908.05895, 2019. [88] A. Pemasiri, K. Nguyen, S. Sridharan, and C. Fookes. Multi-modal semantic image segmentation. Computer Vision and Image Understanding, 202:103085, 2021. [89] F. Pokorny, M. Fivser, F. Graf, P. Marschik, and B. Schuller. Sound and the city: Current perspectives on acoustic geo-sensing in urban environment. Acta Acustica united with Acustica, 105(5), 2019. [90] S. Poria, D. Hazarika, N. Majumder, G. Naik, E. Cambria, and R. Mihalcea. Meld: A multimodal multi-party dataset for emotion recognition in conversations. arXiv preprint arXiv:1810.02508, 2018. [91] B. Rajalingam and R. Priya. Hybrid multimodality medical image fusion technique for feature enhancement in medical diagnosis. International Journal of Engineer- ing Science Invention, 2(Special issue):52–60, 2018. [92] N. Rasiwasia, J. Costa Pereira, E. Coviello, G. Doyle, G. Lanckriet, R. Levy, and N. Vasconcelos. A new approach to cross-modal multimedia retrieval. In Proc. of ACM International Conference on Multimedia (MM), pages 251–260, Firenze, Italy, 2010. [93] S. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konecny, S. Kumar, and H. McMahan. Adaptive federated optimization. In Proc. of International Confer- ence on Learning Representations (ICLR), pages 1–38, Virtual, 2021. [94] G. Roque and V. Padilla. LPWAN based IoT surveillance system for outdoor fire detection. IEEE Access, 8, 2020. [95] M. Saradjian and M. Akhoondzadeh. Thermal anomalies detection before strong earthquakes using interquartile, wavelet and kalman filter methods. Natural Haz- ards and Earth System Sciences, 11(4):1099–1108, 2011. [96] I. Sarker. Machine learning: Algorithms, real-world applications and research di- rections. SN Computer Science, 2(3):1–21, 2021.
[97] F. Sattler, S. Wiedemann, K.-R. Mu¨ller, and W. Samek. Robust and communication-efficient federated learning from non-i.i.d. data. IEEE Transac- tions on Neural Networks and Learning Systems, 31(9), 2020. [98] A. Saxena, A. Khanna, and D. Gupta. Emotion recognition and detection methods: A comprehensive survey. Journal of Artificial Intelligence and Systems, 2(1), 2020. [99] H. Seo, J. Park, S. Oh, M. Bennis, and S.-L. Kim. Federated knowledge distillation. arXiv preprint arXiv:2011.02367, 2020.
[100] S. Sharma, C. Xing, Y. Liu, and Y. Kang. Secure and efficient federated transfer learning. In Proc. of IEEE International Conference on Big Data (Big Data), pages 2569–2576, Los Angeles, CA, 2019. [101] G. Shen, X. Wang, X. Duan, H. Li, and W. Zhu. MEmoR: a dataset for multi- modal emotion reasoning in videos. In Proc. of ACM International Conference on Multimedia (MM), pages 493–502, Seattle, USA, 2020. [102] K. Shih, S. Singh, and D. Hoiem. Where to look: Focus regions for visual question answering. In Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4613–4621, Las Vegas, USA, 2016. [103] U. Sulubacak, O. Caglayan, S.-A. Gronroos, A. Rouhe, D. Elliott, L. Specia, and J. Tiedemann. Multimodal machine translation through visuals and speech. Ma- chine Translation, 34(2):97–147, 2020. [104] C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu. A survey on deep trans- fer learning. In Proc. of International Conference on Artificial Neural Networks (ICANN), pages 270–279, Rhodes, Greece, 2018. [105] J. Tang, R. Shivanna, Z. Zhao, D. Lin, A. Singh, E. Chi, and S. Jain. Understanding and improving knowledge distillation. arXiv preprint arXiv:2002.03532, 2020. [106] Z. Tang, S. Shi, X. Chu, W. Wang, and B. Li. Communication-efficient distributed deep learning: A comprehensive survey. arXiv preprint arXiv:2003.06307, 2020. [107] C. Teague, J. Heller, B. Nevius, A. Carek, S. Mabrouk, F. Garcia-Vicente, O. Inan, and M. Etemadi. A wearable, multimodal sensing system to monitor knee joint health. IEEE Sensors Journal, 20(18):10323–10334, 2020. [108] C. Thapa, M. Chamikara, S. Camtepe, and L. Sun. Splitfed: When federated learn- ing meets split learning. arXiv preprint arXiv:2004.12088, 2020.
[109] H. Tian, Y. Tao, S. Pouyanfar, S.-C. Chen, and M.-L. Shyu. Multimodal deep rep- resentation learning for video classification. World Wide Web, 22(3):1325–1341, 2019. [110] M. Treml, J. Arjona-Medina, T. Unterthiner, et al. Speeding up semantic seg- mentation for autonomous driving. In Proc. of Conference on Neural Information Processing Systems (NeurIPS), 2016. [111] J. Tu, H. Li, X. Yan, M. Ren, Y. Chen, M. Liang, E. Bitar, E. Yumer, and R. Urtasun. Exploring adversarial robustness of multi-sensor perception systems in self driving. arXiv preprint arXiv:2101.06784, 2021. [112] A. Valada, R. Mohan, and W. Burgard. Self-supervised model adaptation for multimodal semantic segmentation. International Journal of Computer Vision, 128(5):1239–1285, 2020. [113] P. Vepakomma, O. Gupta, T. Swedish, and R. Raskar. Split learning for health: Distributed deep learning without sharing raw patient data. arXiv preprint arXiv:1812.00564, 2018. [114] I. Wagner and D. Eckhoff. Technical privacy metrics: a systematic survey. ACM Computing Surveys (CSUR), 51(3):1–38, 2018. [115] H. Wang, P. Cai, R. Fan, Y. Sun, and M. Liu. End-to-end interactive prediction and planning with optical flow distillation for autonomous driving. In Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2229–2238, Virtual, 2021. [116] H. Wang, L. Munoz-Gonzalez, D. Eklund, and S. Raza. Non-iid data re-balancing at IoT edge with peer-to-peer federated learning for anomaly detection. In Proc. of ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec), pages 153–163, Virtual, 2021. [117] H. Wang, D. Zhang, Y. Song, S. Liu, Y. Wang, D. Feng, H. Peng, and W. Cai. Seg- menting neuronal structure in 3D optical microscope images via knowledge distil- lation with teacher-student network. In Proc. of IEEE International Symposium on Biomedical Imaging (ISBI), pages 228–231, Venice, Italy, 2019. [118] J. Wang, Q. Liu, H. Liang, G. Joshi, and H. Poor. Tackling the objective inconsis- tency problem in heterogeneous federated optimization. In Proc. of International Conerence on Neural Information Processing Systems (NeurIPS), 33:7611–7623, 2020.
[119] L. Wang, W. Wang, and B. Li. CMFL: Mitigating communication overhead for federated learning. In Proc. of IEEE International Conference on Distributed Com- puting Systems (ICDCS), pages 954–964, Dallas, USA, 2019. [120] L. Wang and K.-J. Yoon. Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(6), 2021. [121] Y. Wang. Survey on deep multi-modal data analytics: collaboration, rivalry, and fusion. ACM Transactions on Multimedia Computing, Communications, and Ap- plications, 17(1s), 2021. [122] K. Wei, J. Li, M. Ding, C. Ma, H. Yang, F. Farokhi, S. Jin, T. Quek, and H. Poor. Federated learning with differential privacy: Algorithms and performance analysis. IEEE Transactions on Information Forensics and Security, 15:3454–3469, 2020. [123] J. Wu, Q. Liu, Z. Huang, Y. Ning, H. Wang, E. Chen, J. Yi, and B. Zhou. Hi- erarchical personalized federated learning for user modeling. In Proc. of the Web Conference, pages 957–968, Ljubljana, Slovenia, 2021. [124] H. Xiao, K. Rasul, and R. Vollgraf. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017. [125] H. Xu and K. Saenko. Ask, attend and answer: Exploring question-guided spa- tial attention for visual question answering. In Proc. of European Conference on Computer Vision (ECCV), pages 451–466, 2016. [126] M. Xu, X. Wang, X. Zhang, G. Bin, Z. Jia, and K. Chen. Computation-efficient multi-model deep neural network for sleep stage classification. In Proc. of Asia Ser- vice Sciences and Software Engineering Conference (ASSE), pages 1–8, Nagoya, Japan, 2020. [127] D. Yang, Z. Xu, W. Li, et al. Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan. Medical Image Analysis, 70, 2021. [128] H. Yang, H. He, W. Zhang, and X. Cao. Fedsteg: A federated transfer learning framework for secure image steganalysis. IEEE Transactions on Network Science and Engineering, 8(2):1084–1094, 2020. [129] X. Yin, Y. Zhu, and J. Hu. A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions. ACM Computing Surveys (CSUR), 54(6):1–36, 2021.
[130] N. Yoshida, T. Nishio, M. Morikura, K. Yamamoto, and R. Yonetani. Hybrid- FL for wireless networks: Cooperative learning mechanism using non-iid data. In Proc. of IEEE International Conference on Communications (ICC), pages 1–7, Virtual, 2020. [131] G. Yuan, X. Liu, Q. Yan, S. Qiao, Z. Wang, and L. Yuan. Hand gesture recogni- tion using deep feature fusion network based on wearable sensors. IEEE Sensors Journal, 21(1), 2020. [132] B. Yuhas, M. Goldstein, and T. Sejnowski. Integration of acoustic and visual speech signals using neural networks. IEEE Communications Magazine, 27(11):65–71, 1989. [133] S. Zagoruyko and N. Komodakis. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928, 2016. [134] C. Zhang, Z. Yang, X. He, and L. Deng. Multimodal intelligence: Representation learning, information fusion, and applications. IEEE Journal of Selected Topics in Signal Processing, 14(3):478–493, 2020. [135] H. Zhao, X. Qi, X. Shen, J. Shi, and J. Jia. ICNet for real-time semantic segmen- tation on high-resolution images. In Proc. of European Conference on Computer Vision (ECCV), pages 405–420, Munich, Germany, 2018. [136] R. Zhao, Y. Chen, Y. Wang, Y. Shi, and Z. Xue. An efficient and lightweight approach for intrusion detection based on knowledge distillation. In Proc. of IEEE International Conference on Communications (ICC), pages 1–6, Virtual, 2021. [137] Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra. Federated learning with non-iid data. arXiv preprint arXiv:1806.00582, 2018. [138] B. Zhou, Y. Tian, S. Sukhbaatar, A. Szlam, and R. Fergus. Simple baseline for visual question answering. arXiv preprint arXiv:1512.02167, 2015. [139] X. Zhou, W. Liang, I. Kevin, K. Wang, H. Wang, L. Yang, and Q. Jin. Deep- learning-enhanced human activity recognition for internet of healthcare things. IEEE Internet of Things Journal, 7(7), 2020. [140] H. Zhu, J. Xu, S. Liu, and Y. Jin. Federated learning on non-iid data: A survey. Neurocomputing, 465:371–390, 2021.
[141] X. Zhu and A. Goldberg. Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 3(1):1–130, 2009. [142] Z. Zhu, J. Hong, and J. Zhou. Data-free knowledge distillation for heterogeneous federated learning. In Proc. of International Conference on Machine Learning (ICML), pages 12878–12889, Virtual, 2021.
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