Analysis of Federated Learning Paradigm in Medical Domain: Taking COVID-19 as an Application Use Case
Abstract
:1. Introduction
2. Fundamental Concepts Related to FL
2.1. Fundamentals of FL
2.2. Activities of Server and Clients in FL
2.3. Major Taxonomies in FL
3. Fusion of FL and Medical Domain
3.1. Need of FL in Medical Domain
3.2. Architecture of FL in Medical Domain
3.3. Applications of FL in Medical Domain
3.4. Challenges of FL in Medical Domain
4. Potential Applications of FL Technology in the COVID-19 Era
5. FL Synergy with Other Digital Technologies in the Medical Context
6. Future Research Trajectories for FL Ecosystem
- Robust solutions for statistical heterogeneity: In the FL, the data used in local model training can be highly diverse at each site (e.g., different languages used in next-word-prediction tasks) leading to poor convergence of the global model [64]. Since the data mostly violate the IID assumption, the accuracy of the global model minimally improves in each round. Although some strategies (data augmentation, data sharing, etc.) for addressing the non-IID problem in the FL setting have been proposed [65], this topic still needs rigorous work to eliminate all limitations stemming from all categories (i.e., attributes, labels, distributions, and temporal skews) of non-IID data.
- Robust strategies for client and server behavior analysis: In the FL paradigm, it is quite challenging to distinguish between benign and abnormal clients/servers because, in some cases, abnormal clients/servers can behave like real clients/servers, and vice versa. However, clients and servers are two of the most critical components of the FL paradigm, so analysis of their behavior to ensure reliable results and fairness is imperative in the FL setting. To this end, integration of the latest technologies, such as blockchain, RL, and anomaly-detection algorithms with FL, is handy for restricting manipulations of data or parameters, tampering with the training phase, and the possibility of launching collaboration attacks. In the future, it will be interesting to devise robust strategies for client/server behavior analysis to ensure the smooth operation of FL.
- Credible incentive mechanisms for good data contributors: In the FL setting, most clients can have a free ride and can leave the FL system at any time, leading to poor convergence of the global model. To ensure smooth mechanisms, incentive mechanisms for clients have been introduced recently in the FL paradigm. However, the selection of a pool of suitable clients that can contribute good data and stay active throughout the model training process is tricky. To this end, credible incentive mechanisms for selecting good data contributors (and performance analyses) are needed. Furthermore, devising multicriteria-based incentive mechanisms (e.g., activeness and availability of clients, data quality, resource availability, motivation) is a vibrant area of research.
- Intelligent methods for inference-time vulnerability mitigation: After multiple rounds of training, the trained model can be deployed to serve people. However, there are plenty of interference-time vulnerabilities that stem from trained model deployment. For example, the model can yield low accuracy on different versions of the test data carefully crafted by an attacker. The credibility and fairness of the final results produced by the FL model can be low due to the higher diversity in data. Furthermore, scalability and verifiability are two main challenges in this context. To alleviate such issues, intelligent methods are required to lower interference-time vulnerabilities and make FL models trustworthy.
- Practical methods for enhancing security and privacy in FL ecosystems: FL ecosystems are constantly targeted by active/passive attackers to impair model performance [66,67]. Attacks that corrupt/damage global model performance by manipulating either model updates or samples of training data are referred to as poisoning attacks (PAs) [68]. PAs can be classified into two types:
- –
- Data PA: integrity breach in training data → global model corruption.
- –
- Model PA: manipulation of model updates (i.e., training procedure → global model corruption).
- Simultaneous optimization of multiple types of trade-offs in FL systems: There exist multiple types of trade-offs in FL systems such as privacy-accuracy trade-offs, privacy-poising trade-offs, privacy-convergence trade-offs, and privacy-fairness trade-off [70,71]. Recently, researchers have resolved more than two types of trade-offs in FL environments. Wei et al. [72] explored the solution for three different types (e.g., robustness, privacy, and governance) of trade-offs in distributed AI systems. Zhang et al. [73] explored ways to solve three types (security, robustness, and privacy) of trade-offs to make FL more trustworthy. Kang et al. [74] explored ways to resolve privacy, utility, and efficiency objectives in FL environments. To this end, more methods are required that can effectively resolve the trade-offs of different kinds without degrading performance.
- Adoption of data-centric AI approaches to address data quality problems: The application of data-centric approaches https://landing.ai/data-centric-ai/ (accessed on 10 February 2024) to enhance data quality within the framework of COVID-19-oriented FL is very challenging due to the higher diversity in the amount and type of data across hospitals. In the COVID-19 era, there was a huge skew in the data across hospitals such as feature skew, labels skew, quality skew, and quantity skew. However, the solution for quantity skew poses more challenges in server-side aggregation as the local models trained with long-tail distributed data cannot fairly contribute to the global model’s performance [75]. In the conventional FL setting, the data are assumed to be fixed; however, the COVID-19 scenario was different, and data were originating in stream form, posing many challenges in terms of cleaning, transformation, integration, and reduction. It was very challenging to achieve convergence in a reasonable time, particularly when non-IID data and the straggler effect (e.g., some clients have poor computing resources) combined in COVID-19 handling systems [76]. It was very challenging to distinguish between the non-IID COVID-19 data and poisoned COVID-19 data as the effect of both these cases is roughly the same on FL model performance, and there is no way to analyze the local clients’ data. The end-to-end pipeline development (e.g., IoMT) under the FL paradigm posed many technical challenges concerning resource management and equitable use. The selection of suitable AI models for handling diverse aspects (e.g., screening, prediction, projections, diagnosis, classification, etc.) related to the pandemic was also very challenging. Exploring promising solutions for the above-cited pandemic data-related challenges is an interesting area of research.
- Harnessing the potentials of quantum computing to improve FL-based systems: Of late, a new paradigm, namely, quantum FL (QFL) has emerged [77,78], and its potential in the medical domain is yet to be explored. Therefore, it is also one of the promising research areas to be investigated in the context of the medical domain to address performance bottlenecks in the conventional FL paradigm. Lastly, high communication bandwidth, a large number of devices to participate in the training process to achieve a good model, and scalability issues also require low-cost solutions.
7. Concluding Remarks and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Paradigm | Synergy with | Purpose of Synergy |
---|---|---|
FL | DP | Privacy protection of sensitive medical data |
Local DP | Privacy protection of FL parameters/gradients | |
HE | Privacy preservation of data/parameter in transfer | |
Blockchain | Privacy infrastructure to share COVID-19 information [35], resource allocation, self-testing, EWS, poisoning detection [36], etc. | |
Edge CC | Secure analytics of COVID-19 image data | |
Multilayer perceptron | Robust mortality predictive models | |
Support vector machine (SVM) | Prediction of daily COVID-19 cases | |
IoT | COVID-19 screening, medical data acquisition and efficient analysis [37] | |
IoT big data | Fast detection of virus, Enhancing data quality [38] | |
5G-enabled architecture | Classifying severity of COVID-19 | |
IIoT | Accurate detection of COVID-19, personalized healthcare [39] | |
RL | Identifying COVID-19 from medical images, image segmentation [40], decision making [41] | |
NAS | Face mask detection | |
CC | Diagnosis [42], analytics of medical data [43], disease monitoring [44], QoS enhancement of FL-based systems [45] | |
IoMT | Medical DSS for tracking COVID-19, collaboration among medical institutes [46] | |
Transfer learning | Classifying COVID-19 from lung scans, breast cancer classification [47] | |
Access control | To safeguard medical data in BDE | |
Social IoT | Robust contact tracing, on-board training of medical devices [48] | |
SMC | Construction of virus vulnerability map, data protection in dynamic scenarios [49] | |
Watermarking | Privacy protection of gradient information, privacy-preserved data sharing [50] | |
Knowledge distillation | Identifying normality, COVID-19, and pneumonia from X-rays, medical image segmentation [51] | |
ZKPs | Mobile healthcare, sensitive data protection [52] | |
Split learning | Collaborative healthcare analytics [53] | |
Fog computing | Performance enhancement of medical devices [54] | |
ChatGPT | Knowledge enhancement and better QoS [55,56] |
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Hwang, S.O.; Majeed, A. Analysis of Federated Learning Paradigm in Medical Domain: Taking COVID-19 as an Application Use Case. Appl. Sci. 2024, 14, 4100. https://doi.org/10.3390/app14104100
Hwang SO, Majeed A. Analysis of Federated Learning Paradigm in Medical Domain: Taking COVID-19 as an Application Use Case. Applied Sciences. 2024; 14(10):4100. https://doi.org/10.3390/app14104100
Chicago/Turabian StyleHwang, Seong Oun, and Abdul Majeed. 2024. "Analysis of Federated Learning Paradigm in Medical Domain: Taking COVID-19 as an Application Use Case" Applied Sciences 14, no. 10: 4100. https://doi.org/10.3390/app14104100
APA StyleHwang, S. O., & Majeed, A. (2024). Analysis of Federated Learning Paradigm in Medical Domain: Taking COVID-19 as an Application Use Case. Applied Sciences, 14(10), 4100. https://doi.org/10.3390/app14104100