Dataset Information

Challenges and future directions of secure federated learning: a survey.

ABSTRACT: Federated learning came into being with the increasing concern of privacy security, as people's sensitive information is being exposed under the era of big data. It is an algorithm that does not collect users' raw data, but aggregates model parameters from each client and therefore protects user's privacy. Nonetheless, due to the inherent distributed nature of federated learning, it is more vulnerable under attacks since users may upload malicious data to break down the federated learning server. In addition, some recent studies have shown that attackers can recover information merely from parameters. Hence, there is still lots of room to improve the current federated learning frameworks. In this survey, we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated learning. Several open issues and existing solutions in federated learning are discussed. We also point out the future research directions of federated learning.

Electronic supplementary material

Supplementary material is available in the online version of this article at 10.1007/s11704-021-0598-z.

SUBMITTER: Zhang K

PROVIDER: S-EPMC8663756 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Challenges and future directions of secure federated learning: a survey.

Zhang Kaiyue K Song Xuan X Zhang Chenhan C Yu Shui S

Frontiers of computer science 20211210 5

Federated learning came into being with the increasing concern of privacy security, as people's sensitive information is being exposed under the era of big data. It is an algorithm that does not collect users' raw data, but aggregates model parameters from each client and therefore protects user's privacy. Nonetheless, due to the inherent distributed nature of federated learning, it is more vulnerable under attacks since users may upload malicious data to break down the federated learning server ...[more]

PMID: 34909232

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Project description:Background/Objectives: Artificial intelligence (AI) and machine learning (ML) are transforming healthcare by enabling predictive, diagnostic, and therapeutic advancements. Pediatric healthcare presents unique challenges, including limited data availability, developmental variability, and ethical considerations. This narrative review explores the current trends, applications, challenges, and future directions of ML in pediatric healthcare. Methods: A systematic search of the PubMed database was conducted using the query: ("artificial intelligence" OR "machine learning") AND ("pediatric" OR "paediatric"). Studies were reviewed to identify key themes, methodologies, applications, and challenges. Gaps in the research and ethical considerations were also analyzed to propose future research directions. Results: ML has demonstrated promise in diagnostic support, prognostic modeling, and therapeutic planning for pediatric patients. Applications include the early detection of conditions like sepsis, improved diagnostic imaging, and personalized treatment strategies for chronic conditions such as epilepsy and Crohn's disease. However, challenges such as data limitations, ethical concerns, and lack of model generalizability remain significant barriers. Emerging techniques, including federated learning and explainable AI (XAI), offer potential solutions. Despite these advancements, research gaps persist in data diversity, model interpretability, and ethical frameworks. Conclusions: ML offers transformative potential in pediatric healthcare by addressing diagnostic, prognostic, and therapeutic challenges. While advancements highlight its promise, overcoming barriers such as data limitations, ethical concerns, and model trustworthiness is essential for its broader adoption. Future efforts should focus on enhancing data diversity, developing standardized ethical guidelines, and improving model transparency to ensure equitable and effective implementation in pediatric care.

Project description:Enhancing deep learning performance requires extensive datasets. Centralized training raises concerns about data ownership and security. Additionally, large models are often unsuitable for hospitals due to their limited resource capacities. Federated learning (FL) has been introduced to address these issues. However, FL faces challenges such as vulnerability to attacks, non-IID data, reliance on a central server, high communication overhead, and suboptimal model aggregation. Furthermore, FL is not optimized for realistic hospital database environments, where data are dynamically accumulated. To overcome these limitations, we propose federated influencer learning (FIL) as a secure and efficient collaborative learning paradigm. Unlike the server-client model of FL, FIL features an equal-status structure among participants, with an administrator overseeing the overall process. FIL comprises four stages: local training, qualification, screening, and influencing. Local training is similar to vanilla FL, except for the optional use of a shared dataset. In the qualification stage, participants are classified as influencers or followers. During the screening stage, the integrity of the logits from the influencer is examined. If the integrity is confirmed, the influencer shares their knowledge with the others. FIL is more secure than FL because it eliminates the need for model-parameter transactions, central servers, and generative models. Additionally, FIL supports model-agnostic training. These features make FIL particularly promising for fields such as healthcare, where maintaining confidentiality is crucial. Our experiments demonstrated the effectiveness of FIL, which outperformed several FL methods on large medical (X-ray, MRI, and PET) and natural (CIFAR-10) image dataset in a dynamically accumulating database environment, with consistently higher precision, recall, Dice score, and lower standard deviation between participants. In particular, in the PET dataset, FIL achieved about a 40% improvement in Dice score and recall.

Dataset Information

Challenges and future directions of secure federated learning: a survey.

Electronic supplementary material

Publications

Challenges and future directions of secure federated learning: a survey.

Similar Datasets

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