Scilight Press

Author Information

Received: 14 Jun 2025 | Revised: 01 Aug 2025 | Accepted: 13 Aug 2025 | Published: 18 Aug 2025

Abstract

Keywords

federated learning | medical image analysis | trustworthy machine learning | machine unlearning

References 

• 1.
  Act, A. Health insurance portability and accountability act of 1996. Public Law 1996, 104, 191.
• 2.
  Regulation, P. General data protection regulation. Intouch 2018, 25, 1–5.
• 3.
  Nazir, S.; Kaleem, M. Federated learning for medical image analysis with deep neural networks. Diagnostics 2023, 13, 1532.
• 4.
  Guan, H.; Yap, P.-T.; Bozoki, A.; et al. Federated learning for medical image analysis: A survey. Pattern Recognit. 2024, 110424.
• 5.
  Pfitzner, B.; Steckhan, N.; Arnrich, B. Federated learning in a medical context: A systematic literature review. ACM Trans. Internet Technol. 2021, 21, 50.
• 6.
  Ciupek, D.; Malawski, M.; Pieciak, T. Federated learning: A new frontier in the exploration of multi-institutional medical imaging data. arXiv 2025, arXiv:2503.20107.
• 7.
  Sandhu, S.S.; Gorji, H.T.; Tavakolian, P.; et al. Medical imaging applications of federated learning. Diagnostics 2023, 13, 3140.
• 8.
  Kaissis, G.A.; Makowski, M.R.; Ru, D.; et al. Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2020, 2, 305–311.
• 9.
  BMcMahan; Moore, E.; Ramage, D.; et al. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics; PMLR: New York, NY, USA, 2017; pp. 1273–1282.
• 10.
  Glorot, X.; Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Sardinia, Italy, 13–15 May 2010; pp. 249–256.
• 11.
  He, K.; Zhang, X.; Ren, S.; et al. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 7–13 December 2015; pp. 1026–1034.
• 12.
  Kirillov, A.; Mintun, E.; Ravi, N.; et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France, 1–6 October 2023; pp. 4015–4026.
• 13.
  Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 2012, 25.
• 14.
  He, K.; Zhang, X.; Ren, S.; et al. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778.
• 15.
  Huang, G.; Liu, Z.; Van Der Maaten, L.; et al. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 4700–4708.
• 16.
  Cheng, J.; Tian, S.; Yu, L.; et al. Ddu-net: A dual dense u-structure network for medical image segmentation. Appl. Soft Comput. 2022, 126, 109297.
• 17.
  Xu, Y.; Kong, M.; Xie, W.; et al. Deep sequential feature learning in clinical image classification of infectious keratitis. Engineering 2021, 7, 1002–1010.
• 18.
  Sarkar, S.; Min, K.; Ikram, W.; et al. Performing automatic identification and staging of urothelial carcinoma in bladder cancer patients using a hybrid deep-machine learning approach. Cancers 2023, 15, 1673.
• 19.
  Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; et al. An image is worth 16 × 16 words: Transformers for image recognition at scale. arXiv 2020, arXiv:2010.11929.
• 20.
  Chen, J.; Lu, Y.; Yu, Q.; et al. Transunet: Transformers make strong encoders for medical image segmentation. arXiv 2021, arXiv:2102.04306.
• 21.
  Li, T.; Sahu, A.K.; Talwalkar, A.; et al. Federated learning: Challenges, methods, and future directions. IEEE Signal Process. Mag. 2020, 37, 50–60.
• 22.
  Hsu TM, H.; Qi, H.; Brown, M. Measuring the effects of non-identical data distribution for federated visual classification. arXiv 2019, arXiv:1909.06335.
• 23.
  Li, T.; Sahu, A.K.; Zaheer, M.; et al. Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2020, 2, 429–450.
• 24.
  Liu, Z.; Wu, F.; Wang, Y.; et al. Fedcl: Federated contrastive learning for multi-center medical image classification. Pattern Recognit. 2023, 143, 109739.
• 25.
  Shi, M.; Zhou, Y.; Wang, K.; et al. Prior: Personalized prior for reactivating the information overlooked in federated learning. Adv. Neural Inf. Process. Syst. 2023, 36, 28378–28392.
• 26.
  Fu, S.; Yang, Z.; Hu, C.; et al. Personalized federated learning with contrastive momentum. IEEE Trans. Big Data 2024. https://doi.org/10.1109/TBDATA.2024.3403387.
• 27.
  Yang, Z.; Bao, W.; Yuan, D.; et al. Federated learning with nesterov accelerated gradient. IEEE Trans. Parallel Distrib. Syst. 2022, 33, 4863–4873.
• 28.
  Yang, Z.; Fu, S.; Bao, W.; et al. FastSlowMo: Federated learning with combined worker and aggregator momenta. IEEE Trans. Artif. Intell. 2022, 4, 1041–1050.
• 29.
  Abdelmoniem, A.M.; Canini, M. Towards mitigating device heterogeneity in federated learning via adaptive model quantization. In Proceedings of the 1st Workshop on Machine Learning and Systems, Online, UK, 26 April 2021; pp. 96–103.
• 30.
  Zhou, Z.; Luo, G.; Chen, M.; et al. Federated learning for medical image classification: A comprehensive benchmark. arXiv 2025, arXiv:2504.05238.
• 31.
  Huang, Y.; Du, C.; Xue, Z.; et al. What makes multi-modal learning better than single (provably). Adv. Neural Inf. Process. Syst. 2021, 34, 10944–10956.
• 32.
  Shao, W.; Wang, T.; Sun, L.; et al. Multi-task multi-modal learning for joint diagnosis and prognosis of human cancers. Med. Image Anal. 2020, 65, 101795.
• 33.
  Boehm, K.M.; Aherne, E.A.; Ellenson, L.; et al. Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer. Nat. Cancer 2022, 3, 723–733.
• 34.
  Olatunji, I.; Cui, F. Multimodal ai for prediction of distant metastasis in carcinoma patients. Front. Bioinform. 2023, 3, 1131021.
• 35.
  Kim, G.; Moon, S.; Choi, J.-H. Deep learning with multimodal integration for predicting recurrence in patients with non-small cell lung cancer. Sensors 2022, 22, 6594.
• 36.
  Agbley, B.L.Y.; Li, J.; Haq, A.U.; et al. Multimodal melanoma detection with federated learning. In Proceedings of the 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, China, 17–19 December 2021; pp. 238–244.
• 37.
  Cassará; P; Gotta, A.; Valerio, L. Federated feature selection for cyber-physical systems of systems. IEEE Trans. Veh. Technol. 2022, 71, 9937–9950.
• 38.
  Xiong, B.; Yang, X.; Qi, F.; et al. A unified framework for multi-modal federated learning. Neurocomputing 2022, 480, 110–118.
• 39.
  Qayyum, A.; Ahmad, K.; Ahsan, M.A.; et al. Collaborative federated learning for healthcare: Multi-modal COVID-19 diagnosis at the edge. IEEE Open J. Comput. Soc. 2022, 3, 172–184.
• 40.
  Chen, Z.; Liu, Y.; Zhang, Y.; et al. Enhanced multimodal low-rank embedding based feature selection model for multimodal Alzheimer’s disease diagnosis. IEEE Trans. Med. Imaging 2024, 44, 815–827.
• 41.
  Laine, S.; Aila, T. Temporal ensembling for semi-supervised learning. arXiv 2016, arXiv:1610.02242.
• 42.
  Xie, Q.; Dai, Z.; Hovy, E.; et al. Unsupervised data augmentation for consistency training. Adv. Neural Inf. Process. Syst. 2020, 33, 6256–6268.
• 43.
  Li, J.; Xiong, C.; Hoi, S.C. Comatch: Semi-supervised learning with contrastive graph regularization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada, 11–17 October 2021; pp. 9475–9484.
• 44.
  Lee, D.H. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on Challenges in Representation Learning; ICML: Atlanta, GA, USA, 2013; Volume 3, p. 896.
• 45.
  Xie, Q.; Luong, M.-T.; Hovy, E.; et al. Self-training with noisy student improves imagenet classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 10687–10698.
• 46.
  Pham, H.; Dai, Z.; Xie, Q.; et al. Meta pseudo labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021; pp. 11557–11568.
• 47.
  Sohn, K.; Berthelot, D.; Carlini, N.; et al. Fixmatch: Simplifying semi-supervised learning with consistency and confidence. Adv. Neural Inf. Process. Syst. 2020, 33, 596–608.
• 48.
  Chen, J.; Zhang, H.; Mohiaddin, R.; et al. Adaptive hierarchical dual consistency for semi-supervised left atrium segmentation on cross-domain data. IEEE Trans. Med. Imaging 2021, 41, 420–433.
• 49.
  Wang, G.; Zhai, S.; Lasio, G.; et al. Semi-supervised segmentation of radiation-induced pulmonary fibrosis from lung CT scans with multi-scale guided dense attention. IEEE Trans. Med. Imaging 2021, 41, 531–542.
• 50.
  Shi, Y.; Zhang, J.; Ling, T.; et al. Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation. IEEE Trans. Med. Imaging 2021, 41, 608–620.
• 51.
  Liu, Q.; Yang, H.; Dou, Q.; et al. Federated semi-supervised medical image classification via inter-client relation matching. In Medical Image Computing and Computer Assisted Intervention. Proceedings of the MICCAI 2021: 24th International Conference, Strasbourg, France, 27 September–1 October 2021; Springer: Cham, Switzerland, 2021; pp. 325–335.
• 52.
  Liang, X.; Lin, Y.; Fu, H.; et al. Rscfed: Random sampling consensus federated semi-supervised learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022; pp. 10154–10163.
• 53.
  Yang, D.; Xu, Z.; Li, W.; et al. Federated semi-supervised learning for covid region segmentation in chest CT using multi-national data from China, Italy, Japan. Med. Image Anal. 2021, 70, 101992.
• 54.
  Bdair, T.; Navab, N.; Albarqouni, S. Fedperl: Semi-supervised peer learning for skin lesion classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention, Proceedings of the 24th International Conference, Strasbourg, France, 27 September–1 October 2021; Springer: Cham, Switzerland, 2021; pp. 336–346.
• 55.
  Jeong, W.; Yoon, J.; Yang, E.; et al. Federated semi-supervised learning with inter-client consistency & disjoint learning. arXiv 2020, arXiv:2006.12097.
• 56.
  Wu, H.; Zhang, B.; Chen, C.; et al. Federated semi-supervised medical image segmentation via prototype-based pseudo-labeling and contrastive learning. IEEE Trans. Med. Imaging 2023, 43, 649–661.
• 57.
  Ma, Y.; Wang, J.; Yang, J.; et al. Model-heterogeneous semi-supervised federated learning for medical image segmentation. IEEE Trans. Med. Imaging 2024, 43, 1804–1815.
• 58.
  Wang, Y.; Zhang, J.; Kan, M.; et al. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 13–19 June 2020; pp. 12275–12284.
• 59.
  Patel, G.; Dolz, J. Weakly supervised segmentation with cross-modality equivariant constraints. Med. Image Anal. 2022, 77, 102374.
• 60.
  Lempitsky, V.; Kohli, P.; Rother, C.; et al. Image segmentation with a bounding box prior. In Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan, 29 September–2 October 2009; pp. 277–284.
• 61.
  Kervadec, H.; Dolz, J.; Tang, M.; et al. Constrained-cnn losses for weakly supervised segmentation. Med. Image Anal. 2019, 54, 88–99.
• 62.
  Kervadec, H.; Dolz, J.; Wang, S.; et al. Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision. In Medical Imaging with Deep Learning; PMLR: New York, NY, USA, 2020; pp. 365–381.
• 63.
  Hsu, C.-C.; Hsu, K.-J.; Tsai, C.-C.; et al. Weakly supervised instance segmentation using the bounding box tightness prior. Adv. Neural Inf. Process. Syst. 2019, 32.
• 64.
  Wang, J.; Xia, B. Bounding box tightness prior for weakly supervised image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France, 27 September–1 October 2021; pp. 526–536.
• 65.
  Rajchl, M.; Lee, M.C.; Oktay, O.; et al. Deepcut: Object segmentation from bounding box annotations using convolutional neural networks. IEEE Trans. Med. Imaging 2016, 36, 674–683.
• 66.
  Song, C.; Huang, Y.; Ouyang, W.; et al. Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 3136–3145.
• 67.
  Laradji, I.; Rodriguez, P.; Manas, O.; et al. A weakly supervised consistency-based learning method for COVID-19 segmentation in CT images. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Virtua, 5–9 January 2021; pp. 2453–2462.
• 68.
  Lin, D.; Dai, J.; Jia, J.; et al. Scribblesup: Scribble-supervised convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 26 June–1 July 2016; pp. 3159–3167.
• 69.
  Liu, X.; Yuan, Q.; Gao, Y.; et al. Weakly supervised segmentation of COVID-19 infection with scribble annotation on CT images. Pattern Recognit. 2022, 122, 108341.
• 70.
  Luo, X.; Hu, M.; Liao, W.; et al. Scribble-supervised medical image segmentation via dual-branch network and dynamically mixed pseudo labels supervision. In Proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, Singapore, 18–22 September 2022; pp. 528–538.
• 71.
  Liu, X.; Wang, S.; Zhang, Y.; et al. Scribble-supervised meibomian glands segmentation in infrared images. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 2022, 18, 88.
• 72.
  Zhu, M.; Chen, Z.; Yuan, Y. Feddm: Federated weakly supervised segmentation via annotation calibration and gradient de-conflicting. IEEE Trans. Med. Imaging 2023, 42, 1632–1643.
• 73.
  Wicaksana, J.; Yan, Z.; Zhang, D.; et al. Fedmix: Mixed supervised federated learning for medical image segmentation. IEEE Trans. Med. Imaging 2022, 42, 1955–1968.
• 74.
  Lin, L.; Liu, Y.; Wu, J.; et al. Fedlppa: Learning personalized prompt and aggregation for federated weakly-supervised medical image segmentation. IEEE Trans. Med. Imaging 2024, 44, 1127–1139.
• 75.
  Pang, G.; Shen, C.; Cao, L.; et al. Deep learning for anomaly detection: A review. ACM Comput. Surv. 2021, 54, 38.
• 76.
  Cai, Y.; Zhang, W.; Chen, H.; et al. Medianomaly: A comparative study of anomaly detection in medical images. Med. Image Anal. 2025, 102, 103500.
• 77.
  Schlegl, T.; Seebo, P.; Waldstein, S.M.; et al. f-anogan: Fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 2019, 54, 30–44.
• 78.
  Baur, C.; Denner, S.; Wiestler, B.; et al. Autoencoders for unsupervised anomaly segmentation in brain MR images: A comparative study. Med. Image Anal. 2021, 69, 101952.
• 79.
  Goodfellow, I.J.; Pouget-Abadie, J.; Mirza, M.; et al. Generative adversarial nets. Adv. Neural Inf. Process. Syst. 2014, 27.
• 80.
  Schlu, H.M.; Tan, J.; Hou, B.; et al. Natural synthetic anomalies for self-supervised anomaly detection and localization. In Proceedings of the 17th European Conference on Computer Vision, Tel Aviv, Israel, 23–27 October 2022; pp. 474–489.
• 81.
  Tian, Y.; Liu, F.; Pang, G.; et al. Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images. Med. Image Anal. 2023, 90, 102930.
• 82.
  Zhang, X.; Li, S.; Li, X.; et al. Destseg: Segmentation guided denoising student-teacher for anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 17–24 June 2023; pp. 3914–3923.
• 83.
  Roth, K.; Pemula, L.; Zepeda, J.; et al. Towards total recall in industrial anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022; pp. 14318–14328.
• 84.
  Bercea, C.I.; Wiestler, B.; Rueckert, D.; et al. Federated disentangled representation learning for unsupervised brain anomaly detection. Nat. Mach. Intell. 2022, 4, 685–695.
• 85.
  Gupta, D.; Kayode, O.; Bhatt, S.; et al. Hierarchical federated learning based anomaly detection using digital twins for smart healthcare. In Proceedings of the 2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC), Atlanta, GA, USA, 13–15 December 2021; pp. 16–25.
• 86.
  He, Y.; Ding, X.; Tang, Y.; et al. Unsupervised multivariate time series anomaly detection by feature decoupling in federated learning scenarios. IEEE Trans. Artif. Intell. 2025, 6, 2013–2026.
• 87.
  Ganin, Y.; Lempitsky, V. Unsupervised domain adaptation by backpropagation. In Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 7–9 July 2015; pp. 1180–1189.
• 88.
  Tzeng, E.; Hoffman, J.; Saenko, K.; et al. Adversarial discriminative domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 7167–7176.
• 89.
  Volpi, R.; Morerio, P.; Savarese, S.; et al. Adversarial feature augmentation for unsupervised domain adaptation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 5495–5504.
• 90.
  Taigman, Y.; Polyak, A.; Wolf, L. Unsupervised cross-domain image generation. arXiv 2016, arXiv:1611.02200.
• 91.
  Liu, M.-Y.; Tuzel, O. Coupled generative adversarial networks. Adv. Neural Inf. Process. Syst. 2016, 29.
• 92.
  Ji, W.; Chung, A.C. Unsupervised domain adaptation for medical image segmentation using transformer with meta attention. IEEE Trans. Med. Imaging 2023, 43, 820–831.
• 93.
  Deng, S.; Chen, Y.; Huang, W.; et al. Unsupervised domain adaptation for EM image denoising with invertible networks. IEEE Trans. Med. Imaging 2024, 44, 92–105.
• 94.
  Peng, X.; Huang, Z.; Zhu, Y.; et al. Federated adversarial domain adaptation. arXiv 2019, arXiv:1911.02054.
• 95.
  Xiao, Y.; Guo, Y.; Zhu, H.; et al. An unsupervised federated domain adaptation method based on knowledge distillation. IEEE Trans. Neural Netw. Learn. Syst. 2024, 36, 10993–11007.
• 96.
  El Ouadrhiri, A.; Abdelhadi, A. Differential privacy for deep and federated learning: A survey. IEEE Access 2022, 10, 22359–22380.
• 97.
  Acar, A.; Aksu, H.; Uluagac, A.S.; et al. A survey on homomorphic encryption schemes: Theory and implementation. ACM Comput. Surv. 2018, 51, 79.
• 98.
  Muthalakshmi, M.; Jeyapal, K.; Vinoth, M.; et al. Federated learning for secure and privacy-preserving medical image analysis in decentralized healthcare systems. In Proceedings of the 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 7–9 August 2024; pp. 1442–1447.
• 99.
  Yang, J.; Shi, R.; Wei, D.; et al. Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification. Sci. Data 2023, 10, 41.
• 100.
  Tanim, S.A.; Mridha, M.; Safran, M.; et al. Secure federated learning for parkinson’s disease: Non-iid data partitioning and homomorphic encryption strategies. IEEE Access 2024, 12, 127309–127327.
• 101.
  Adams, J.L.; Dinesh, K.; Snyder, C.W.; et al. Pd-biostamprc21: Parkinson’s disease accelerometry dataset from five wearable sensor study. IEEE Dataport 2020. https://doi.org/10.21227/g2g8-1503.
• 102.
  RAhmed; Maddikunta, P.K.R.; Gadekallu, T.R.; et al. Efficient differential privacy enabled federated learning model for detecting covid-19 disease using chest x-ray images. Front. Med. 2024, 11, 1409314.
• 103.
  Shastri, S.; Kansal, I.; Kumar, S.; et al. Cheximagenet: A novel architecture for accurate classification of covid-19 with chest x-ray digital images using deep convolutional neural networks. Health Technol. 2022, 12, 193–204.
• 104.
  Mo, F.; Haddadi, H.; Katevas, K.; et al. Ppfl: Privacypreserving federated learning with trusted execution environments. In Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, Virtual, 24 June–2 July 2021; pp. 94–108.
• 105.
  Makkar, A.; Santosh, K. Securefed: Federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays. Int. J. Mach. Learn. Cybern. 2023, 14, 2659–2670.
• 106.
  Lessage, X.; Collier, L.; Van Ouytsel, C.-H.B.; et al. Secure federated learning applied to medical imaging with fully homomorphic encryption. In Proceedings of the 2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC), Houston, TX, USA, 7–9 February 2024; pp. 1–12.
• 107.
  Rieyan, S.A.; News, M.R.K.; Rahman, A.M.; et al. An advanced data fabric architecture leveraging homomorphic encryption and federated learning. Inf. Fusion 2024, 102, 102004.
• 108.
  Haripriya, R.; Khare, N.; Pandey, M. Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings. Sci. Rep. 2025, 15, 12482.
• 109.
  Sun, G.; Shu, H.; Shao, F.; et al. Fkd-med: Privacy-aware, communication-optimized medical image segmentation via federated learning and model lightweighting through knowledge distillation. IEEE Access 2024, 12, 33687–33704.
• 110.
  Hinton, G.; Vinyals, O.; Dean, J. Distilling the knowledge in a neural network. arXiv 2015, arXiv:1503.02531.
• 111.
  Huang, H.; Zhang, L.; Sun, C.; et al. Distributed pruning towards tiny neural networks in federated learning. In Proceedings of the 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS), Hong Kong, China, 18–21 July 2023; pp. 190–201.
• 112.
  Huang, H.; Zhuang, W.; Chen, C.; et al. Fedmef: Towards memory-efficient federated dynamic pruning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 16–22 June 2024; pp. 27548–27557.
• 113.
  Huang, H.; Yang, H.; Chen, Y.; et al. Fedrts: Federated robust pruning via combinatorial thompson sampling. arXiv 2025, arXiv:2501.19122.
• 114.
  Xie, H.; Zhang, Y.; Zhou, Z.; et al. Privacy-preserving medical data collaborative modeling: A differential privacy enhanced federated learning framework. J. Knowl. Learn. Sci. Technol. 2024, 3, 340–350.
• 115.
  Yang, Z.; Fu, S.; Bao, W.; et al. Hierarchical federated learning with momentum acceleration in multi-tier networks. IEEE Trans. Parallel Distrib. Syst. 2023, 34, 2629–2641.
• 116.
  Xie, L.; Lin, M.; Liu, S.; et al. pflfe: Cross-silo personalized federated learning via feature enhancement on medical image segmentation. In Proceedings of the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, Marrakesh, Morocco, 6–10 October 2024; pp. 599–610.
• 117.
  Shapley, L.S. A value for n-person games. In Contributions to the Theory of Games, Volume II; Princeton University Press: Princeton, NJ, USA, 2016; pp. 307–318.
• 118.
  Wei, S.; Tong, Y.; Zhou, Z.; et al. Efficient and fair data valuation for horizontal federated learning. In Federated Learning: Privacy and Incentive; Springer: Cham, Switzerland, 2020; pp. 139–152.
• 119.
  Liu, Z.; Chen, Y.; Yu, H.; et al. Gtg-shapley: Efficient and accurate participant contribution evaluation in federated learning. ACM Trans. Intell. Syst. Technol. 2022, 13, 60.
• 120.
  Fan, Z.; Fang, H.; Zhou, Z.; et al. Improving fairness for data valuation in horizontal federated learning. In Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia, 9–12 May 2022; pp. 2440–2453.
• 121.
  Kim, H.; Park, J.; Bennis, M.; et al. Blockchained on-device federated learning. IEEE Commun. Lett. 2020, 24, 1279–1283.
• 122.
  Lo, S.K.; Liu, Y.; Lu, Q.; et al. Toward trustworthy ai: Blockchain-based architecture design for accountability and fairness of federated learning systems. IEEE Internet Things J. 2023, 10, 3276–3284.
• 123.
  Otoum, S.; Ridhawi, I.A.; Mouftah, H. Securing critical IoT infrastructures with blockchain-supported federated learning. IEEE Internet Things J. 2022, 9, 2592–2601.
• 124.
  Myrzashova, R.; Alsamhi, S.H.; Shvetsov, A.V.; et al. Blockchain meets federated learning in healthcare: A systematic review with challenges and opportunities. IEEE Internet Things J. 2023, 10, 14418–14437.
• 125.
  Zhu, J.; Cao, J.; Saxena, D.; et al. Blockchain-empowered federated learning: Challenges, solutions, and future directions. ACM Comput. Surv. 2023, 55, 240.
• 126.
  Yang, Z.; Fu, S.; Bao, W.; et al. Hierarchical federated learning with adaptive momentum in multi-tier networks. In Proceedings of the 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS), Hong Kong, China, 18–21 July 2023; pp. 499–510.
• 127.
  Liu, G.; Ma, X.; Yang, Y.; et al. Federaser: Enabling efficient client-level data removal from federated learning models. In Proceedings of the 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS), Tokyo, Japan, 25–28 June 2021; pp. 1–10.
• 128.
  Liu, Y.; Xu, L.; Yuan, X.; et al. The right to be forgotten in federated learning: An efficient realization with rapid retraining. In Proceedings of the IEEE INFOCOM 2022-IEEE Conference on Computer Communications, London, UK, 2–5 May 2022; pp. 1749–1758.
• 129.
  Chen, M.; Zhang, Z.; Wang, T.; et al. When machine unlearning jeopardizes privacy. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual, 15–19 November 2021; pp. 896–911.
• 130.
  Zhao, Y.; Wang, P.; Qi, H.; et al. Federated unlearning with momentum degradation. IEEE Internet Things J. 2023, 11, 8860–8870.
• 131.
  Bertran, M.; Tang, S.; Kearns, M.; et al. Reconstruction attacks on machine unlearning: Simple models are vulnerable. Adv. Neural Inf. Process. Syst. 2024, 37, 104995–105016.