Abstract
The growing interest in advanced data-intensive models for healthcare applications presents several challenges and opportunities. Federated learning (FL) emerges as an attractive solution to allow decentralized nodes to collectively train shared machine learning models without the need of transmitting sensitive data to a central database. This can safeguard privacy while effectively leveraging the distributed computational resources available in the cloud-edge continuum. In health informatics, the need for robust privacy-preserving mechanisms is paramount, especially when the nodes of the FL system are associated with datasets from individual patients, as opposed to the case of databases that include many patients (such as those available from hospitals). This need becomes particularly significant when addressing diagnoses and predictive analytics in personalized medicine, precision medicine, risk stratification, and longitudinal monitoring. We explore the applications of FL frameworks in the context of cloud-edge in healthcare. We identify real-world settings to assess the benefits and challenges of personalized federated learning. These include data imbalance issues, usability, promoting replicability, improving security, minimizing environmental impact (greenness), and optimizing overall efficiency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adnan, M., Kalra, S., Cresswell, J.C., Taylor, G.W., Tizhoosh, H.R.: Federated learning and differential privacy for medical image analysis. Sci. Rep. 12, 1953 (2022). https://doi.org/10.1038/s41598-022-05539-7
Dayan, I., Roth, H.R., Zhong, A., et al.: Federated learning for predicting clinical outcomes in patients with COVID-19. Nat. Med. 27, 1735–1743 (2021). https://doi.org/10.1038/s41591-021-01506-3
Zhang, W., et al.: Dynamic-fusion-based federated learning for COVID-19 detection. IEEE Internet Things J. 8, 15884–15891 (2021). https://doi.org/10.1109/JIOT.2021.3056185
Xue, Z., et al.: A resource-constrained and privacy-preserving edge-computing-enabled clinical decision system: a federated reinforcement learning approach. IEEE Internet Things J. 8, 9122–9138 (2021). https://doi.org/10.1109/JIOT.2021.3057653
Lim, W.Y.B., et al.: Dynamic contract design for federated learning in smart healthcare applications. IEEE Internet Things J. 8, 16853–16862 (2021). https://doi.org/10.1109/JIOT.2020.3033806
Wu, Q., Chen, X., Zhou, Z., Zhang, J.: FedHome: cloud-edge based personalized federated learning for in-home health monitoring. http://arxiv.org/abs/2012.07450 (2020)
Tan, A.Z., Yu, H., Cui, L., Yang, Q.: Towards personalized federated learning. IEEE Trans. Neural Netw. Learning Syst. 34, 9587–9603 (2023). https://doi.org/10.1109/TNNLS.2022.3160699
Ge, Y., Zhou, Y., Jia, L.: Adaptive personalized federated learning with one-shot screening. IEEE Internet Things J. (2024). https://doi.org/10.1109/JIOT.2023.3346900
Wu, Q., He, K., Chen, X.: Personalized federated learning for intelligent IoT applications: a cloud-edge based framework. IEEE Open J. Comput. Soc. 1, 35–44 (2020). https://doi.org/10.1109/OJCS.2020.2993259
Chen, Y., Wang, J., Yu, C., Gao, W., Qin, X.: FedHealth: a federated transfer learning framework for wearable healthcare. http://arxiv.org/abs/1907.09173 (2021)
Li, X., Jiang, M., Zhang, X., Kamp, M., Dou, Q.: FedBN: federated learning on non-iid features via local batch normalization. http://arxiv.org/abs/2102.07623 (2021)
Srivastava, U.C., Singh, A., Kumar, D.K.S.: Intracranial hemorrhage detection using neural network based methods with federated learning. http://arxiv.org/abs/2005.08644 (2022)
Unknown: Federated learning: protecting data at the source. Intel Labs (2023). https://www.intel.com/content/www/us/en/research/news/federated-learning-protecting-data-at-the-source.html. Accessed 25 Jan 2024
Cho, H., Mathur, A., Kawsar, F.: FLAME: federated learning across multi-device environments. In: Proceedings of ACM Interaction Mobile Wearable Ubiquitous Technology, vol. 6, pp. 1–29 (2022). https://doi.org/10.1145/3550289
Shamsian, A., Navon, A., Fetaya, E., Chechik, G.: personalized federated learning using hypernetworks. http://arxiv.org/abs/2103.04628 (2021)
Lu, W., et al.: Personalized federated learning with adaptive batchnorm for healthcare. http://arxiv.org/abs/2112.00734 (2022)
Rudovic, O., et al.: Personalized federated deep learning for pain estimation from face images. http://arxiv.org/abs/2101.04800 (2021)
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-IID data (2018). https://doi.org/10.48550/arXiv.1806.00582
Li, Q., Diao, Y., Chen, Q., He, B.: Federated learning on non-IID data silos: an experimental study. http://arxiv.org/abs/2102.02079 (2021)
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: a review. IEEE Trans. Knowl. Data Eng. (2018). https://doi.org/10.1109/TKDE.2018.2876857
Casado, F.E., Lema, D., Criado, M.F., Iglesias, R., Regueiro, C.V., Barro, S.: Concept drift detection and adaptation for federated and continual learning. Multimed Tools Appl. 81, 3397–3419 (2022). https://doi.org/10.1007/s11042-021-11219-x
Matsuda, K., Sasaki, Y., Xiao, C., Onizuka, M.: Benchmark for personalized federated learning. IEEE Open J. Comput. Soc. 5, 2–13 (2024). https://doi.org/10.1109/OJCS.2023.3332351
Yang, L., Huang, J., Lin, W., Cao, J.: Personalized federated learning on non-IID Data via group-based meta-learning. ACM Trans. Knowl. Discov. Data 17, 1–20 (2023). https://doi.org/10.1145/3558005
Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: challenges, methods, and future directions. IEEE Signal Process. Mag. 37, 50–60 (2020). https://doi.org/10.1109/MSP.2020.2975749
Xu, J., Glicksberg, B.S., Su, C., Walker, P., Bian, J., Wang, F.: Federated learning for healthcare informatics. http://arxiv.org/abs/1911.06270, (2020)
Li, J., et al.: A federated learning based privacy-preserving smart healthcare system. IEEE Trans. Ind. Inf. 18, 2021–2031 (2022). https://doi.org/10.1109/TII.2021.3098010
Li, X., Huang, K., Yang, W., Wang, S., Zhang, Z.: On the convergence of FedAvg on Non-IID data. http://arxiv.org/abs/1907.02189 (2020)
Zhu, H., Zhang, H., Jin, Y.: From federated learning to federated neural architecture search: a survey. http://arxiv.org/abs/2009.05868 (2020)
Shokri, R., Strobel, M., Zick, Y.: On the privacy risks of model explanations. http://arxiv.org/abs/1907.00164 (2021)
Yaacoub, J.-P.A., Noura, H.N., Salman, O.: Security of federated learning with IoT systems: issues, limitations, challenges, and solutions. Internet Things Cyber-Phys. Syst. 3, 155–179 (2023). https://doi.org/10.1016/j.iotcps.2023.04.001
Mammen, P.M.: Federated learning: opportunities and challenges. http://arxiv.org/abs/2101.05428 (2021)
Thwal, C.M., Thar, K., Tun, Y.L., Hong, C.S.: Attention on personalized clinical decision support system: federated learning approach. In: 2021 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 141–147. IEEE, Jeju Island (2021). https://doi.org/10.1109/BigComp51126.2021.00035
Zheng, S., Cao, Y., Yoshikawa, M., Li, H., Yan, Q.: FL-Market: trading private models in federated learning. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1525–1534 (2022). https://doi.org/10.1109/BigData55660.2022.10020232
Zhan, Y., Zhang, J., Hong, Z., Wu, L., Li, P., Guo, S.: A survey of incentive mechanism design for federated learning. IEEE Trans. Emerg. Topics Comput. (2021). https://doi.org/10.1109/TETC.2021.3063517
Acknowledgment
This research is supported by the European Union – Next Generation EU, in the context of the National Recovery and Resilience Plan Investment”, Project Age-It (Ageing Well in an Ageing Society).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bochicchio, M., Zeleke, S.N. (2024). Personalized Federated Learning in Edge-Cloud Continuum for Privacy-Preserving Health Informatics: Opportunities and Challenges. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 203. Springer, Cham. https://doi.org/10.1007/978-3-031-57931-8_36
Download citation
DOI: https://doi.org/10.1007/978-3-031-57931-8_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-57930-1
Online ISBN: 978-3-031-57931-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)