## 7 Concluding Remarks and Future Work
Eng._**2022**, _10_, 2864-2880. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Secure and Efficient Federated Learning with Provable Performance Guarantees via Stochastic Quantization. _IEEE Trans. Inf. Forensics Secur._**2024**, _19_, 4070-4085. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] IoMT: A COVID-19 Healthcare System driven by Federated Learning and Blockchain. _IEEE J. Biomed. Health Inform._**2023**, _27_, 823-834. [CrossRef] [PubMed]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] BPFL: Blockchain-Based Privacy-Preserving Federated Learning against Poisoning Attack. _Inf. Sci._**2024**, _665_, 120377. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Harnessing Federated Learning for Efficient Analysis of Large-Scale Healthcare Image Datasets in IoT-Enabled Healthcare Systems. _Int. J. Intell. Syst. Appl. Eng._**2024**, _12_, 253-263. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. Synergies between centralized and federated approaches to data quality: A report from the national COVID cohort collaborative. _J. Am. Med. Inform. Assoc._**2022**, _29_, 609-618. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] FedCure: A Heterogeneity-Aware Personalized Federated Learning Framework for Intelligent Healthcare Applications in IoMT Environments. _IEEE Access_**2024**, _12_, 15867-15883. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Model-level Attention and Batch-instance Style Normalization for Federated Learning on Medical Image Segmentation. _Inf. Fusion_**2020**, _107_, 102348. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Federated sequential decision making: Bayesian optimization, reinforcement learning, and beyond. In _Federated Learning_; Elsevier: Amsterdam, The Netherlands, 2024; pp. 257-279. * [PERSON] et al. (2021) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Federated Patient Similarity Network for Data-Driven Diagnosis of COVID-19 Patients. In Proceedings of the 2021 IEEE/ACS 18 th International Conference on Computer Systems and Applications (AICCSA), Tangier, Morocco, 30 November-3 December 2021; pp. 1-6. * [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] FedHealthFog: A federated learning-enabled approach towards healthcare analytics over fog computing platform. _Heliyon_**2024**, _10_, e26416. [CrossRef]* _[PERSON] and [PERSON] (2024)_ [PERSON]; [PERSON] [PERSON] A Heart Disease Prognosis Pipeline for the Edge using Federated Learning. _e-Prime-Adv. Electr. Eng. Electron. Energy_**2024**, \(7\), 100490. * _[PERSON] et al. (2024)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Enhancing quality of service through federated learning in edge-cloud architecture. _Ad Hoc Netw._**2024**, _156_, 103430. [CrossRef]
* _[PERSON] et al. (2024)_ [PERSON]; [PERSON]; [PERSON], [PERSON]; [PERSON]; [PERSON]; [PERSON] RFLPV: A robust federated learning scheme with privacy preservation and verifiable aggregation in IoMT. _Inf. Fusion_**2024**, _102_, 102029. [CrossRef]
* _[PERSON] et al. (2024)_ [PERSON] [PERSON]; [PERSON]; et al. Privacy-Preserving Breast Cancer Classification: A Federated Transfer Learning Approach. _J. Imaging Inform. Med._ **2024**, _Online ahead of print._ [CrossRef] [PubMed]
* _[PERSON] et al. (2024)_ [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Federated learning for IoT devices: Enhancing TinyML with on-board training. _Inf. Fusion_**2024**, _104_, 102189. [CrossRef]
* _[PERSON] et al.