Page 97 - AIH-2-4

Artificial Intelligence in Health Federated learning health stack against pandemics

doi: 10.1109/ICKECS61492.2024.10616691 Proceedings of the AAAI Conference on Artificial Intelligence.
Vol. 6. Washington, DC: AAAI Press; 2023. p. 7314-7322.
57. Deng L. The mnist database of handwritten digit images
for machine learning research. IEEE Sign Process Mag. doi: 10.48550/arXiv.2212.02758
2012;29(6):141-142.
65. Babar M, Qureshi B, Koubaa A. Investigating the impact
doi: 10.1109/MSP.2012.2211477 of data heterogeneity on the performance of federated
learning algorithm using medical imaging. PLoS One.
58. Jimenez GM, Solans D, Heikkila M, et al. Non-IID Data
in Federated Learning: A Survey with Taxonomy, Metrics, 2024;19(5):e0302539.
Methods, Frameworks and Future Directions. United States: doi: 10.1371/journal.pone.0302539
Cornell University; 2024.
66. ℤhao Y, Li M, Lai L, Suda N, Civin D, Chandra V. Federated
doi: 10.48550/arXiv.2411.12377 Learning with Non-Iid Data. United States: IEEE; 2018.
59. Ye M, Fang X, Du B, Yuen PC, Tao D. Heterogeneous doi: 10.48550/arXiv.1806.00582
federated learning: State-of-the-art and research challenges. 67. Chang Q, Yan ℤ, ℤhou M, et al. Mining multi-center
ACM Comput Surv. 2023;56(3):1-44.
heterogeneous medical data with distributed synthetic
doi: 10.48550/arXiv.2307.10616 learning. Nat Commun. 2023;14(1):5510.
60. Gao D, Yao X, Yang Q. A Survey on Heterogeneous Federated doi: 10.1038/s41467-023-40687-y
Learning. United States: Cornell University; 2022.
68. Kossen T, Hirzel MA, Madai VI, et al. Toward sharing
doi: 10.48550/arXiv.2210.04505 brain images: Differentially private TOF-MRA images with
segmentation labels using generative adversarial networks.
61. Müller-Franzes G, Niehues JM, Khader F, et al. A multimodal
comparison of latent denoising diffusion probabilistic Front Artif Intell. 2022;5:813842.
models and generative adversarial networks for medical doi: 10.3389/frai.2022.813842
image synthesis. Sci Rep. 2023;13:12098.
69. Jiale ℤH, Chengcheng ℤH, Xiaobing SU, et al. Membership
doi: 10.1038/s41598-023-39278-0 inference attack and defense method in federated learning
based on GAN[J]. J Commun. 2023;44(5):193-205.
62. ℤadeh FS, Molani S, Orouskhani M, Rezaei M, Shafiei M,
Abbasi H. Generative Adversarial Networks for Brain Images doi: 10.11959/j.issn.1000-436x.2023094
Synthesis: A Review. United States: Cornell University; 2023.
70. Xia J, ℤhang Y, Yue ℤ, Hu M, Wei X, Chen M. HierarchyFL:
doi: 10.48550/arXiv.2305.15421 Heterogeneous Federated Learning via Hierarchical Self-
Distillation. United States: Cornell University; 2022.
63. Legler T, Hegiste V, Anwar A, Ruskowski M. Addressing
heterogeneity in federated learning: challenges and solutions doi: 10.48550/arXiv.2212.02006
for a shared production environment. Procedia Comput Sci. 71. Yang H, Li J, Hao M, ℤhang W, He H, Sangaiah AK. An
2025;253:2831-2840.
efficient personalized federated learning approach in
doi: 10.48550/arXiv.2408.09556 heterogeneous environments: A reinforcement learning
perspective. Sci Rep. 2024;14:28877.
64. Dai Y, Chen ℤ, Li J, Heinecke S, Sun L, Xu R. Tackling data
heterogeneity in federated learning with class prototypes. In: doi: 10.1038/s41598-024-80048-3

Volume 2 Issue 4 (2025) 91 doi: 10.36922/AIH025080013