* [23] A. Sayed, Y. Himeur, A. Alsalemi, F. Bensaali, and A. Amira, “Intelligent edge-based recommender system for internet of energy applications,” _IEEE Systems Journal_ , vol. 16, no. 3, pp. 5001–5010, 2021.
  * [24] Q. Yang, Y. Liu, Y. Cheng, Y. Kang, T. Chen, and H. Yu, “Federated learning,” _Synthesis Lectures on Artificial Intelligence and Machine Learning_ , vol. 13, no. 3, pp. 1–207, 2019.
  * [25] O. Gupta and R. Raskar, “Distributed learning of deep neural network over multiple agents,” _Journal of Network and Computer Applications_ , vol. 116, pp. 1–8, 2018.
  * [26] A. R. Khan, A. Zoha, L. Mohjazi, H. Sajid, Q. Abbasi, and M. A. Imran, “When federated learning meets vision: An outlook on opportunities and challenges,” in _EAI International Conference on Body Area Networks_. Springer, 2021, pp. 308–319.
  * [27] K. Giorgas and I. Varlamis, “Online federated learning with imbalanced class distribution,” in _24th Pan-Hellenic Conference on Informatics_ , 2020, pp. 91–95.
  * [28] S. Abuadbba, K. Kim, M. Kim, C. Thapa, S. A. Camtepe, Y. Gao, H. Kim, and S. Nepal, “Can we use split learning on 1d cnn models for privacy preserving training?” in _Proceedings of the 15th ACM Asia Conference on Computer and Communications Security_ , 2020, pp. 305–318.
  * [29] M. S. Jere, T. Farnan, and F. Koushanfar, “A taxonomy of attacks on federated learning,” _IEEE Security & Privacy_, vol. 19, no. 2, pp. 20–28, 2020\.
  * [30] J. Zhang, Y. Chen, and H. Li, “Privacy leakage of adversarial training models in federated learning systems,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2022, pp. 108–114.
  * [31] Y. Himeur, A. Sayed, A. Alsalemi, F. Bensaali, A. Amira, I. Varlamis, M. Eirinaki, C. Sardianos, and G. Dimitrakopoulos, “Blockchain-based recommender systems: Applications, challenges and future opportunities,” _Computer Science Review_ , vol. 43, p. 100439, 2022.
  * [32] Y. Lu, X. Huang, Y. Dai, S. Maharjan, and Y. Zhang, “Blockchain and federated learning for privacy-preserved data sharing in industrial iot,” _IEEE Transactions on Industrial Informatics_ , vol. 16, no. 6, pp. 4177–4186, 2019\.
  * [33] O. A. Wahab, A. Mourad, H. Otrok, and T. Taleb, “Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems,” _IEEE Communications Surveys & Tutorials_, vol. 23, no. 2, pp. 1342–1397, 2021.
  * [34] Q. Li, Z. Wen, Z. Wu, S. Hu, N. Wang, Y. Li, X. Liu, and B. He, “A survey on federated learning systems: vision, hype and reality for data privacy and protection,” _IEEE Transactions on Knowledge and Data Engineering_ , 2021\.
  * [35] M. Aledhari, R. Razzak, R. M. Parizi, and F. Saeed, “Federated learning: A survey on enabling technologies, protocols, and applications,” _IEEE Access_ , vol. 8, pp. 140 699–140 725, 2020.
  * [36] D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. V. Poor, “Federated learning for internet of things: A comprehensive survey,” _IEEE Communications Surveys & Tutorials_, vol. 23, no. 3, pp. 1622–1658, 2021.
  * [37] S. AbdulRahman, H. Tout, H. Ould-Slimane, A. Mourad, C. Talhi, and M. Guizani, “A survey on federated learning: The journey from centralized to distributed on-site learning and beyond,” _IEEE Internet of Things Journal_ , vol. 8, no. 7, pp. 5476–5497, 2020.
  * [38] Y. Cheng, Y. Liu, T. Chen, and Q. Yang, “Federatfed learning for privacy-preserving ai,” _Communications of the ACM_ , vol. 63, no. 12, pp. 33–36, 2020.
  * [39] S. Yang, B. Ren, X. Zhou, and L. Liu, “Parallel distributed logistic regression for vertical federated learning without third-party coordinator,” _arXiv preprint arXiv:1911.09824_ , 2019.
  * [40] S. Sharma, C. Xing, Y. Liu, and Y. Kang, “Secure and efficient federated transfer learning,” in _2019 IEEE International Conference on Big Data (Big Data)_. IEEE, 2019, pp. 2569–2576.
  * [41] G. Bjelobaba, A. Savić, T. Tošić, I. Stefanović, and B. Kocić, “Collaborative learning supported by blockchain technology as a model for improving the educational process,” _Sustainability_ , vol. 15, no. 6, p. 4780, 2023.
  * [42] Y. Liu, X. Zhang, Y. Kang, L. Li, T. Chen, M. Hong, and Q. Yang, “Fedbcd: A communication-efficient collaborative learning framework for distributed features,” _IEEE Transactions on Signal Processing_ , vol. 70, pp. 4277–4290, 2022.
  * [43] E. Gabrielli, G. Pica, and G. Tolomei, “A survey on decentralized federated learning,” _arXiv preprint arXiv:2308.04604_ , 2023.
  * [44] J. Konečnỳ, H. B. McMahan, D. Ramage, and P. Richtárik, “Federated optimization: Distributed machine learning for on-device intelligence,” _arXiv preprint arXiv:1610.02527_ , 2016.
  * [45] V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar, “Federated multi-task learning,” _Advances in neural information processing systems_ , vol. 30, 2017.
  * [46] A. Linardos, K. Kushibar, S. Walsh, P. Gkontra, and K. Lekadir, “Federated learning for multi-center imaging diagnostics: A study in cardiovascular disease,” _arXiv preprint arXiv:2107.03901_ , 2021.
  * [47] Z. Chen, M. Zhu, C. Yang, and Y. Yuan, “Personalized retrogress-resilient framework for real-world medical federated learning,” in _International Conference on Medical Image Computing and Computer-Assisted Intervention_. Springer, 2021, pp. 347–356.
  * [48] K. Doshi and Y. Yilmaz, “Federated learning-based driver activity recognition for edge devices,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2022, pp. 3338–3346.
  * [49] H. Zhu, “On the relationship between (secure) multi-party computation and (secure) federated learning,” _arXiv preprint arXiv:2008.02609_ , 2020.
  * [50] D. Byrd and A. Polychroniadou, “Differentially private secure multi-party computation for federated learning in financial applications,” in _Proceedings of the First ACM International Conference on AI in Finance_ , 2020, pp. 1–9.
  * [51] R. Kanagavelu, Z. Li, J. Samsudin, Y. Yang, F. Yang, R. S. M. Goh, M. Cheah, P. Wiwatphonthana, K. Akkarajitsakul, and S. Wang, “Two-phase multi-party computation enabled privacy-preserving federated learning,” in _2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)_. IEEE, 2020, pp. 410–419.
  * [52] A. Triastcyn and B. Faltings, “Federated learning with bayesian differential privacy,” in _2019 IEEE International Conference on Big Data (Big Data)_. IEEE, 2019, pp. 2587–2596.
  * [53] X. Wu, Y. Zhang, M. Shi, P. Li, R. Li, and N. N. Xiong, “An adaptive federated learning scheme with differential privacy preserving,” _Future Generation Computer Systems_ , vol. 127, pp. 362–372, 2022.
  * [54] U. Shah, I. Dave, J. Malde, J. Mehta, and S. Kodeboyina, “Maintaining privacy in medical imaging with federated learning, deep learning, differential privacy, and encrypted computation,” in _2021 6th International Conference for Convergence in Technology (I2CT)_. IEEE, 2021, pp. 1–6.
  * [55] M. Adnan, S. Kalra, J. C. Cresswell, G. W. Taylor, and H. R. Tizhoosh, “Federated learning and differential privacy for medical image analysis,” _Scientific reports_ , vol. 12, no. 1, pp. 1–10, 2022.
  * [56] O. Choudhury, A. Gkoulalas-Divanis, T. Salonidis, I. Sylla, Y. Park, G. Hsu, and A. Das, “Differential privacy-enabled federated learning for sensitive health data,” _arXiv preprint arXiv:1910.02578_ , 2019.
  * [57] A. Ziller, D. Usynin, R. Braren, M. Makowski, D. Rueckert, and G. Kaissis, “Medical imaging deep learning with differential privacy,” _Scientific Reports_ , vol. 11, no. 1, pp. 1–8, 2021.
  * [58] L. Zhang, J. Xu, P. Vijayakumar, P. K. Sharma, and U. Ghosh, “Homomorphic encryption-based privacy-preserving federated learning in iot-enabled healthcare system,” _IEEE Transactions on Network Science and Engineering_ , pp. 1–17, 2022.
  * [59] D. Stripelis, H. Saleem, T. Ghai, N. Dhinagar, U. Gupta, C. Anastasiou, G. Ver Steeg, S. Ravi, M. Naveed, P. M. Thompson _et al._ , “Secure neuroimaging analysis using federated learning with homomorphic encryption,” in _17th International Symposium on Medical Information Processing and Analysis_ , vol. 12088. SPIE, 2021, pp. 351–359.
  * [60] R. Kumar, J. Kumar, A. A. Khan, H. Ali, C. M. Bernard, R. U. Khan, S. Zeng _et al._ , “Blockchain and homomorphic encryption based privacy-preserving model aggregation for medical images,” _Computerized Medical Imaging and Graphics_ , vol. 102, p. 102139, 2022.
  * [61] T. S. Brisimi, R. Chen, T. Mela, A. Olshevsky, I. C. Paschalidis, and W. Shi, “Federated learning of predictive models from federated electronic health records,” _International journal of medical informatics_ , vol. 112, pp. 59–67, 2018.
  * [62] Y. Zhao, H. Liu, H. Li, P. Barnaghi, and H. Haddadi, “Semi-supervised federated learning for activity recognition,” _arXiv preprint arXiv:2011.00851_ , 2020.
  * [63] A. Grammenos, R. Mendoza Smith, J. Crowcroft, and C. Mascolo, “Federated principal component analysis,” _Advances in Neural Information Processing Systems_ , vol. 33, pp. 6453–6464, 2020.
  * [64] H. H. Kumar, V. Karthik, and M. K. Nair, “Federated k-means clustering: A novel edge ai based approach for privacy preservation,” in _2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)_. IEEE, 2020, pp. 52–56.
  * [65] H. Kassem, D. Alapatt, P. Mascagni, C. AI4SafeChole, A. Karargyris, and N. Padoy, “Federated cycling (fedcy): Semi-supervised federated learning of surgical phases,” _IEEE Transactions on Medical Imaging_ , pp. 1–1, 2022\.
  * [66] Y. Rehman, Y. Gao, J. Shen, P. de Gusmão, and N. Lane, “Federated self-supervised learning for video understanding,” _Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)_ , vol. 13691 LNCS, pp. 506–522, 2022, cited By 0.
  * [67] I. Dave, C. Chen, and M. Shah, “Spact: Self-supervised privacy preservation for action recognition,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , vol. 2022-June. IEEE Computer Society, 2022, pp. 20 132–20 141, cited By 3.
  * [68] S. Li, Y. Mao, J. Li, Y. Xu, J. Li, X. Chen, S. Liu, and X. Zhao, “Fedutn: federated self-supervised learning with updating target network,” _Applied Intelligence_ , 2022, cited By 0.
  * [69] D. Shome and T. Kar, “Fedaffect: Few-shot federated learning for facial expression recognition,” in _Proceedings of the IEEE/CVF International Conference on Computer Vision_ , 2021, pp. 4168–4175.
  * [70] H. Kheddar, M. Hemis, Y. Himeur, D. Megías, and A. Amira, “Deep learning for diverse data types steganalysis: A review,” _arXiv preprint arXiv:2308.04522_ , 2023.
  * [71] W. Ji, J. Li, M. Zhang, Y. Piao, and H. Lu, “Accurate rgb-d salient object detection via collaborative learning,” in _Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVIII 16_. Springer, 2020, pp. 52–69.
  * [72] Q. Fan, D.-P. Fan, H. Fu, C.-K. Tang, L. Shao, and Y.-W. Tai, “Group collaborative learning for co-salient object detection,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2021, pp. 12 288–12 298.
  * [73] S. Teeparthi, V. Jatla, M. S. Pattichis, S. Celedón-Pattichis, and C. LópezLeiva, “Fast hand detection in collaborative learning environments,” in _International Conference on Computer Analysis of Images and Patterns_. Springer, 2021, pp. 445–454.
  * [74] S. Teeparthi, “Long term object detection and tracking in collaborative learning environments,” _arXiv preprint arXiv:2106.07556_ , 2021.
  * [75] C. Huang and R. Nevatia, “High performance object detection by collaborative learning of joint ranking of granules features,” in _2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition_. IEEE, 2010, pp. 41–48.
  * [76] X. Fang, Z. Kuang, R. Zhang, X. Shao, and H. Wang, “Collaborative learning in bounding box regression for object detection,” _Pattern Recognition Letters_ , 2021.
  * [77] Y. Zhou, X. He, L. Huang, L. Liu, F. Zhu, S. Cui, and L. Shao, “Collaborative learning of semi-supervised segmentation and classification for medical images,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2019, pp. 2079–2088.
  * [78] J. Wang, J. Yao, Y. Zhang, and R. Zhang, “Collaborative learning for weakly supervised object detection,” _arXiv preprint arXiv:1802.03531_ , 2018.
  * [79] D. Zhang, J. Han, L. Zhao, and D. Meng, “Leveraging prior-knowledge for weakly supervised object detection under a collaborative self-paced curriculum learning framework,” _International Journal of Computer Vision_ , vol. 127, no. 4, pp. 363–380, 2019.
  * [80] S. Chen, D. Shao, X. Shu, C. Zhang, and J. Wang, “FCC-Net: A full-coverage collaborative network for weakly supervised remote sensing object detection,” _Electronics_ , vol. 9, no. 9, p. 1356, 2020.
  * [81] Y. Liang, G. Qin, M. Sun, J. Qin, J. Yan, and Z. Zhang, “Semantic and detail collaborative learning network for salient object detection,” _Neurocomputing_ , vol. 462, pp. 478–490, 2021.
  * [82] J. Seo and H. Park, “Object recognition in very low resolution images using deep collaborative learning,” _IEEE Access_ , vol. 7, pp. 134 071–134 082, 2019.
  * [83] Q. Guo, X. Wang, Y. Wu, Z. Yu, D. Liang, X. Hu, and P. Luo, “Online knowledge distillation via collaborative learning,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2020, pp. 11 020–11 029.
  * [84] P. Yu and Y. Liu, “Federated object detection: Optimizing object detection model with federated learning,” in _Proceedings of the 3rd International Conference on Vision, Image and Signal Processing_ , 2019, pp. 1–6.
  * [85] H. Choi and I. V. Bajić, “Deep feature compression for collaborative object detection,” in _2018 25th IEEE International Conference on Image Processing (ICIP)_. IEEE, 2018, pp. 3743–3747.
  * [86] 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.
  * [87] Y. He, Y. Kang, J. Luo, L. Fan, and Q. Yang, “A hybrid self-supervised learning framework for vertical federated learning,” _arXiv preprint arXiv:2208.08934_ , 2022.
  * [88] W. Zhuang, Y. Wen, and S. Zhang, “Divergence-aware federated self-supervised learning,” _arXiv preprint arXiv:2204.04385_ , 2022.
  * [89] A. Saeed, F. D. Salim, T. Ozcelebi, and J. Lukkien, “Federated self-supervised learning of multisensor representations for embedded intelligence,” _IEEE Internet of Things Journal_ , vol. 8, no. 2, pp. 1030–1040, 2020.
  * [90] R. Yan, L. Qu, Q. Wei, S.-C. Huang, L. Shen, D. Rubin, L. Xing, and Y. Zhou, “Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging,” _IEEE Transactions on Medical Imaging_ , 2023.
  * [91] R. Zhu, K. Yin, H. Xiong, H. Tang, and G. Yin, “Masked face detection algorithm in the dense crowd based on federated learning,” _Wireless Communications and Mobile Computing_ , vol. 2021, 2021.
  * [92] Y. Himeur, S. Al-Maadeed, H. Kheddar, N. Al-Maadeed, K. Abualsaud, A. Mohamed, and T. Khattab, “Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization,” _Engineering Applications of Artificial Intelligence_ , vol. 119, p. 105698, 2023.
  * [93] A. B. Sada, M. A. Bouras, J. Ma, H. Runhe, and H. Ning, “A distributed video analytics architecture based on edge-computing and federated learning,” in _2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)_. IEEE, 2019, pp. 215–220.
  * [94] F. Concone, C. Ferdico, G. L. Re, and M. Morana, “A federated learning approach for distributed human activity recognition,” in _2022 IEEE International Conference on Smart Computing (SMARTCOMP)_. IEEE, 2022, pp. 269–274.
  * [95] Y. Liu, J. Nie, X. Li, S. H. Ahmed, W. Y. B. Lim, and C. Miao, “Federated learning in the sky: Aerial-ground air quality sensing framework with uav swarms,” _IEEE Internet of Things Journal_ , 2020.
  * [96] Y. Liu, A. Huang, Y. Luo, H. Huang, Y. Liu, Y. Chen, L. Feng, T. Chen, H. Yu, and Q. Yang, “Fedvision: An online visual object detection platform powered by federated learning,” in _Proceedings of the AAAI Conference on Artificial Intelligence_ , vol. 34, 2020, pp. 13 172–13 179.
  * [97] A. Catalfamo, A. Celesti, M. Fazio, G. Randazzo, and M. Villari, “A platform for federated learning on the edge: a video analysis use case,” in _2022 IEEE Symposium on Computers and Communications (ISCC)_ , 2022, pp. 1–7.
  * [98] P. Jain, S. Goenka, S. Bagchi, B. Banerjee, and S. Chaterji, “Federated action recognition on heterogeneous embedded devices,” _arXiv preprint arXiv:2107.12147_ , 2021.
  * [99] B. Zhang, J. Wang, J. Fu, and J. Xia, “Driver action recognition using federated learning,” in _2021 the 7th International Conference on Communication and Information Processing (ICCIP)_ , 2021, pp. 74–77.
  * [100] Z. Shi, L. Zhang, Y. Liu, X. Cao, Y. Ye, M.-M. Cheng, and G. Zheng, “Crowd counting with deep negative correlation learning,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2018, pp. 5382–5390.
  * [101] A. Zhang, J. Shen, Z. Xiao, F. Zhu, X. Zhen, X. Cao, and L. Shao, “Relational attention network for crowd counting,” in _Proceedings of the IEEE/CVF international conference on computer vision_ , 2019, pp. 6788–6797.
  * [102] Y. Jiang, R. Cong, C. Shu, A. Yang, Z. Zhao, and G. Min, “Federated learning based mobile crowd sensing with unreliable user data,” in _2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)_. IEEE, 2020, pp. 320–327.
  * [103] X. Bao, C. Su, Y. Xiong, W. Huang, and Y. Hu, “Flchain: A blockchain for auditable federated learning with trust and incentive,” in _2019 5th International Conference on Big Data Computing and Communications (BIGCOM)_. IEEE, 2019, pp. 151–159.
  * [104] B. R. Kiran, D. M. Thomas, and R. Parakkal, “An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos,” _Journal of Imaging_ , vol. 4, no. 2, p. 36, 2018.
  * [105] S. Singh, S. Bhardwaj, H. Pandey, and G. Beniwal, “Anomaly detection using federated learning,” in _Proceedings of International Conference on Artificial Intelligence and Applications_. Springer, 2021, pp. 141–148.
  * [106] S. Bharti, A. McGibney, and T. O’Gorman, “Edge-enabled federated learning for vision based product quality inspection,” in _2022 33rd Irish Signals and Systems Conference (ISSC)_. IEEE, 2022, pp. 1–6.
  * [107] A. Esteva, K. Chou, S. Yeung, N. Naik, A. Madani, A. Mottaghi, Y. Liu, E. Topol, J. Dean, and R. Socher, “Deep learning-enabled medical computer vision,” _NPJ digital medicine_ , vol. 4, no. 1, pp. 1–9, 2021.
  * [108] M. J. Sheller, G. A. Reina, B. Edwards, J. Martin, and S. Bakas, “Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation,” in _International MICCAI Brainlesion Workshop_. Springer, 2018, pp. 92–104.
  * [109] X. Li, Y. Gu, N. Dvornek, L. H. Staib, P. Ventola, and J. S. Duncan, “Multi-site fmri analysis using privacy-preserving federated learning and domain adaptation: Abide results,” _Medical Image Analysis_ , vol. 65, p. 101765, 2020.
  * [110] I. Dayan, H. R. Roth, A. Zhong, A. Harouni, A. Gentili, A. Z. Abidin, A. Liu, A. B. Costa, B. J. Wood, C.-S. Tsai _et al._ , “Federated learning for predicting clinical outcomes in patients with covid-19,” _Nature medicine_ , vol. 27, no. 10, pp. 1735–1743, 2021.
  * [111] I. Feki, S. Ammar, Y. Kessentini, and K. Muhammad, “Federated learning for covid-19 screening from chest x-ray images,” _Applied Soft Computing_ , vol. 106, p. 107330, 2021.
  * [112] A. E. Cetinkaya, M. Akin, and S. Sagiroglu, “A communication efficient federated learning approach to multi chest diseases classification,” in _2021 6th International Conference on Computer Science and Engineering (UBMK)_. IEEE, 2021, pp. 429–434.
  * [113] B. Yan, B. Liu, L. Wang, Y. Zhou, Z. Liang, M. Liu, and C.-Z. Xu, “Fedcm: A real-time contribution measurement method for participants in federated learning,” in _2021 International Joint Conference on Neural Networks (IJCNN)_. IEEE, 2021, pp. 1–8.
  * [114] H. R. Roth, K. Chang, P. Singh, N. Neumark, W. Li, V. Gupta, S. Gupta, L. Qu, A. Ihsani, B. C. Bizzo _et al._ , “Federated learning for breast density classification: A real-world implementation,” in _Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning_. Springer, 2020, pp. 181–191.
  * [115] Z. Yan, J. Wicaksana, Z. Wang, X. Yang, and K.-T. Cheng, “Variation-aware federated learning with multi-source decentralized medical image data,” _IEEE Journal of Biomedical and Health Informatics_ , 2020.
  * [116] S. Silva, B. A. Gutman, E. Romero, P. M. Thompson, A. Altmann, and M. Lorenzi, “Federated learning in distributed medical databases: Meta-analysis of large-scale subcortical brain data,” in _2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019)_. IEEE, 2019, pp. 270–274.
  * [117] M. J. Sheller, B. Edwards, G. A. Reina, J. Martin, S. Pati, A. Kotrotsou, M. Milchenko, W. Xu, D. Marcus, R. R. Colen _et al._ , “Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data,” _Scientific reports_ , vol. 10, no. 1, pp. 1–12, 2020\.
  * [118] P. Guo, P. Wang, J. Zhou, S. Jiang, and V. M. Patel, “Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2021, pp. 2423–2432.
  * [119] M. Y. Lu, R. J. Chen, D. Kong, J. Lipkova, R. Singh, D. F. Williamson, T. Y. Chen, and F. Mahmood, “Federated learning for computational pathology on gigapixel whole slide images,” _Medical image analysis_ , vol. 76, p. 102298, 2022.
  * [120] W. Li, F. Milletarì, D. Xu, N. Rieke, J. Hancox, W. Zhu, M. Baust, Y. Cheng, S. Ourselin, M. J. Cardoso _et al._ , “Privacy-preserving federated brain tumour segmentation,” in _International workshop on machine learning in medical imaging_. Springer, 2019, pp. 133–141.
  * [121] C. I. Bercea, B. Wiestler, D. Rueckert, and S. Albarqouni, “Feddis: Disentangled federated learning for unsupervised brain pathology segmentation,” _arXiv preprint arXiv:2103.03705_ , 2021.
  * [122] A. E. Cetinkaya, M. Akin, and S. Sagiroglu, “Improving performance of federated learning based medical image analysis in non-iid settings using image augmentation,” in _2021 International Conference on Information Security and Cryptology (ISCTURKEY)_. IEEE, 2021, pp. 69–74.
  * [123] B. Han, R. Jhaveri, H. Wang, D. Qiao, and J. Du, “Application of robust zero-watermarking scheme based on federated learning for securing the healthcare data,” _IEEE Journal of Biomedical and Health Informatics_ , 2021\.
  * [124] B. G. Tekgul, Y. Xia, S. Marchal, and N. Asokan, “Waffle: Watermarking in federated learning,” in _2021 40th International Symposium on Reliable Distributed Systems (SRDS)_. IEEE, 2021, pp. 310–320.
  * [125] X. Liu, S. Shao, Y. Yang, K. Wu, W. Yang, and H. Fang, “Secure federated learning model verification: A client-side backdoor triggered watermarking scheme,” in _2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)_. IEEE, 2021, pp. 2414–2419.
  * [126] D. Połap, G. Srivastava, and K. Yu, “Agent architecture of an intelligent medical system based on federated learning and blockchain technology,” _Journal of Information Security and Applications_ , vol. 58, p. 102748, 2021\.
  * [127] A. Ziller, D. Usynin, N. Remerscheid, M. Knolle, M. Makowski, R. Braren, D. Rueckert, and G. Kaissis, “Differentially private federated deep learning for multi-site medical image segmentation,” _arXiv preprint arXiv:2107.02586_ , 2021.
  * [128] K. Guo, N. Li, J. Kang, and J. Zhang, “Towards efficient federated learning-based scheme in medical cyber-physical systems for distributed data,” _Software: Practice and Experience_ , vol. 51, no. 11, pp. 2274–2289, 2021.
  * [129] M. Jiang, Z. Wang, and Q. Dou, “Harmofl: Harmonizing local and global drifts in federated learning on heterogeneous medical images,” _arXiv preprint arXiv:2112.10775_ , 2021.
  * [130] Q. Liu, C. Chen, J. Qin, Q. Dou, and P.-A. Heng, “Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2021, pp. 1013–1023.
  * [131] B. Sun, H. Huo, Y. Yang, and B. Bai, “Partialfed: Cross-domain personalized federated learning via partial initialization,” _Advances in Neural Information Processing Systems_ , vol. 34, 2021.
  * [132] H.-P. Wang, S. U. Stich, Y. He, and M. Fritz, “Progfed: Effective, communication, and computation efficient federated learning by progressive training,” _arXiv preprint arXiv:2110.05323_ , 2021.
  * [133] J. Luo and S. Wu, “Fedsld: Federated learning with shared label distribution for medical image classification,” _arXiv preprint arXiv:2110.08378_ , 2021\.
  * [134] G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer, and H. Huisman, “Computer-aided detection of prostate cancer in mri,” _IEEE transactions on medical imaging_ , vol. 33, no. 5, pp. 1083–1092, 2014.
  * [135] H. Zhang, J. Bosch, and H. H. Olsson, “End-to-end federated learning for autonomous driving vehicles,” in _2021 International Joint Conference on Neural Networks (IJCNN)_. IEEE, 2021, pp. 1–8.
  * [136] A. Nguyen, T. Do, M. Tran, B. X. Nguyen, C. Duong, T. Phan, E. Tjiputra, and Q. D. Tran, “Deep federated learning for autonomous driving,” in _2022 IEEE Intelligent Vehicles Symposium (IV)_. IEEE, 2022, pp. 1824–1830.
  * [137] X. Zhou, W. Liang, J. She, Z. Yan, I. Kevin, and K. Wang, “Two-layer federated learning with heterogeneous model aggregation for 6g supported internet of vehicles,” _IEEE Transactions on Vehicular Technology_ , vol. 70, no. 6, pp. 5308–5317, 2021.
  * [138] L. U. Khan, Y. K. Tun, M. Alsenwi, M. Imran, Z. Han, and C. S. Hong, “A dispersed federated learning framework for 6g-enabled autonomous driving cars,” _IEEE Transactions on Network Science and Engineering_ , 2022.
  * [139] Y. Li, X. Tao, X. Zhang, J. Liu, and J. Xu, “Privacy-preserved federated learning for autonomous driving,” _IEEE Transactions on Intelligent Transportation Systems_ , vol. 23, no. 7, pp. 8423–8434, 2021.
  * [140] I. Donevski, J. J. Nielsen, and P. Popovski, “On addressing heterogeneity in federated learning for autonomous vehicles connected to a drone orchestrator,” _Frontiers in Communications and Networks_ , vol. 2, p. 709946, 2021.
  * [141] A. M. Elbir, B. Soner, S. Çöleri, D. Gündüz, and M. Bennis, “Federated learning in vehicular networks,” in _2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom)_. IEEE, 2022, pp. 72–77.
  * [142] D. Jallepalli, N. C. Ravikumar, P. V. Badarinath, S. Uchil, and M. A. Suresh, “Federated learning for object detection in autonomous vehicles,” in _2021 IEEE Seventh International Conference on Big Data Computing Service and Applications (BigDataService)_. IEEE, 2021, pp. 107–114.
  * [143] K. Xie, Z. Zhang, B. Li, J. Kang, D. Niyato, S. Xie, and Y. Wu, “Efficient federated learning with spike neural networks for traffic sign recognition,” _IEEE Transactions on Vehicular Technology_ , vol. 71, no. 9, pp. 9980–9992, 2022.
  * [144] F. Sattler, S. Wiedemann, K.-R. Müller, and W. Samek, “Robust and communication-efficient federated learning from non-iid data,” _IEEE transactions on neural networks and learning systems_ , vol. 31, no. 9, pp. 3400–3413, 2019.
  * [145] F. Haddadpour, M. M. Kamani, A. Mokhtari, and M. Mahdavi, “Federated learning with compression: Unified analysis and sharp guarantees,” in _International Conference on Artificial Intelligence and Statistics_. PMLR, 2021, pp. 2350–2358.
  * [146] W. Luping, W. Wei, and L. Bo, “Cmfl: Mitigating communication overhead for federated learning,” in _2019 IEEE 39th international conference on distributed computing systems (ICDCS)_. IEEE, 2019, pp. 954–964.
  * [147] I. Varlamis, C. Sardianos, C. Chronis, G. Dimitrakopoulos, Y. Himeur, A. Alsalemi, F. Bensaali, and A. Amira, “Using big data and federated learning for generating energy efficiency recommendations,” _International Journal of Data Science and Analytics_ , pp. 1–17, 2022.
  * [148] M. Tang, X. Ning, Y. Wang, J. Sun, Y. Wang, H. Li, and Y. Chen, “Fedcor: Correlation-based active client selection strategy for heterogeneous federated learning,” in _2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)_ , 2022, pp. 10 092–10 101.
  * [149] L. Qu, Y. Zhou, P. P. Liang, Y. Xia, F. Wang, E. Adeli, L. Fei-Fei, and D. Rubin, “Rethinking architecture design for tackling data heterogeneity in federated learning,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2022, pp. 10 061–10 071.
  * [150] M. Mendieta, T. Yang, P. Wang, M. Lee, Z. Ding, and C. Chen, “Local learning matters: Rethinking data heterogeneity in federated learning,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2022, pp. 8397–8406.
  * [151] H. Bousbiat, R. Bousselidj, Y. Himeur, A. Amira, F. Bensaali, F. Fadli, W. Mansoor, and W. Elmenreich, “Crossing roads of federated learning and smart grids: Overview, challenges, and perspectives,” _arXiv preprint arXiv:2304.08602_ , 2023.
  * [152] M. Domínguez-Morales, J. P. Domínguez-Morales, Á. Jiménez-Fernández, A. Linares-Barranco, and G. Jiménez-Moreno, “Stereo matching in address-event-representation (aer) bio-inspired binocular systems in a field-programmable gate array (fpga),” _Electronics_ , vol. 8, no. 4, p. 410, 2019.
  * [153] O. Azzouzi, M. Anane, M. Koudil, M. Issad, and Y. Himeur, “Novel area-efficient and flexible architectures for optimal ate pairing on fpga,” _arXiv preprint arXiv:2308.04261_ , 2023.
  * [154] D. Li and J. Wang, “Fedmd: Heterogenous federated learning via model distillation,” _arXiv preprint arXiv:1910.03581_ , 2019.
  * [155] J. Pang, Y. Huang, Z. Xie, Q. Han, and Z. Cai, “Realizing the heterogeneity: A self-organized federated learning framework for iot,” _IEEE Internet of Things Journal_ , vol. 8, no. 5, pp. 3088–3098, 2020.
  * [156] A. Alsalemi, Y. Himeur, F. Bensaali, and A. Amira, “Smart sensing and end-users’ behavioral change in residential buildings: An edge-based internet of energy perspective,” _IEEE Sensors Journal_ , vol. 21, no. 24, pp. 27 623–27 631, 2021.
  * [157] H. Zhu, J. Xu, S. Liu, and Y. Jin, “Federated learning on non-iid data: A survey,” _Neurocomputing_ , vol. 465, pp. 371–390, 2021.
  * [158] H. Wang, Z. Kaplan, D. Niu, and B. Li, “Optimizing federated learning on non-iid data with reinforcement learning,” in _IEEE INFOCOM 2020-IEEE Conference on Computer Communications_. IEEE, 2020, pp. 1698–1707.
  * [159] C. Briggs, Z. Fan, and P. Andras, “Federated learning with hierarchical clustering of local updates to improve training on non-iid data,” in _2020 International Joint Conference on Neural Networks (IJCNN)_. IEEE, 2020, pp. 1–9.
  * [160] H. Bousbiat, Y. Himeur, I. Varlamis, F. Bensaali, and A. Amira, “Neural load disaggregation: Meta-analysis, federated learning and beyond,” _Energies_ , vol. 16, no. 2, p. 991, 2023.
  * [161] S. Pouriyeh, O. Shahid, R. M. Parizi, Q. Z. Sheng, G. Srivastava, L. Zhao, and M. Nasajpour, “Secure smart communication efficiency in federated learning: Achievements and challenges,” _Applied Sciences_ , vol. 12, no. 18, p. 8980, 2022.
  * [162] L. Lyu, H. Yu, X. Ma, L. Sun, J. Zhao, Q. Yang, and P. S. Yu, “Privacy and robustness in federated learning: Attacks and defenses,” _arXiv preprint arXiv:2012.06337_ , 2020.
  * [163] S. Shen, S. Tople, and P. Saxena, “Auror: Defending against poisoning attacks in collaborative deep learning systems,” in _Proceedings of the 32nd Annual Conference on Computer Security Applications_ , 2016, pp. 508–519.
  * [164] S. Andreina, G. A. Marson, H. Möllering, and G. Karame, “Baffle: Backdoor detection via feedback-based federated learning,” in _2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)_. IEEE, 2021, pp. 852–863.
  * [165] Y. Qi, M. S. Hossain, J. Nie, and X. Li, “Privacy-preserving blockchain-based federated learning for traffic flow prediction,” _Future Generation Computer Systems_ , vol. 117, pp. 328–337, 2021.
  * [166] C. He, A. D. Shah, Z. Tang, D. F. N. Sivashunmugam, K. Bhogaraju, M. Shimpi, L. Shen, X. Chu, M. Soltanolkotabi, and S. Avestimehr, “Fedcv: a federated learning framework for diverse computer vision tasks,” _arXiv preprint arXiv:2111.11066_ , 2021.
  * [167] J. Luo, X. Wu, Y. Luo, A. Huang, Y. Huang, Y. Liu, and Q. Yang, “Real-world image datasets for federated learning,” _arXiv preprint arXiv:1910.11089_ , 2019.
  * [168] S. S. Sohail, F. Farhat, Y. Himeur, M. Nadeem, D. Ø. Madsen, Y. Singh, S. Atalla, and W. Mansoor, “Decoding chatgpt: A taxonomy of existing research, current challenges, and possible future directions,” _Journal of King Saud University-Computer and Information Sciences_ , p. 101675, 2023.
  * [169] F. Farhat, E. S. Silva, H. Hassani, D. Ø. Madsen, S. S. Sohail, Y. Himeur, M. A. Alam, and A. Zafar, “Analyzing the scholarly footprint of chatgpt: mapping the progress and identifying future trends,” 2023.
  * [170] S. S. Sohail, F. Farhat, Y. Himeur, M. Nadeem, D. Ø. Madsen, Y. Singh, S. Atalla, and W. Mansoor, “The future of gpt: A taxonomy of existing chatgpt research, current challenges, and possible future directions,” _Current Challenges, and Possible Future Directions (April 8, 2023)_ , 2023\.
  * [171] S. S. Sohail, D. Ø. Madsen, Y. Himeur, and M. Ashraf, “Using chatgpt to navigate ambivalent and contradictory research findings on artificial intelligence,” _Available at SSRN 4413913_ , 2023.