CloudyFL

Gong, Qingyuan; Ruan, Hui; Chen, Yang; Su, Xiang

doi:10.1145/3539491.3539592

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Han et al [64] introduced a secure approach that preserves the accuracy of the medical data and the results verified its robustness to conventional and geometric attacks. Gong et al [65] proposed a cloud based FL model for user behaviour sensing where LSTM is introduced to deal with the non-IID (Independent and Identically Distributed) problem of the distributed training data with its performance better than the comparing models. To address the unfairness problem in FL, Siniosoglou et al [66] proposed an unsupervised fairness method to identify defective models with promising results.…”

Section: Mixed Hybrid and Miscellaneous Approachesmentioning

confidence: 99%

A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems

Gu,

Sabrina,

Fan

et al. 2023

IJERPH

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Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the requirement of data sharing. In healthcare systems, FL allows Medical Internet of Things (MIoT) devices and electronic health records (EHRs) to be trained locally without sending patients data to the central server. This allows healthcare decisions and diagnoses based on datasets from all participants, as well as streamlining other healthcare processes. In terms of user data privacy, this technology allows collaborative training without the need of sharing the local data with the central server. However, there are privacy challenges in FL arising from the fact that the model updates are shared between the client and the server which can be used for re-generating the client’s data, breaching privacy requirements of applications in domains like healthcare. In this paper, we have conducted a review of the literature to analyse the existing privacy and security enhancement methods proposed for FL in healthcare systems. It has been identified that the research in the domain focuses on seven techniques: Differential Privacy, Homomorphic Encryption, Blockchain, Hierarchical Approaches, Peer to Peer Sharing, Intelligence on the Edge Device, and Mixed, Hybrid and Miscellaneous Approaches. The strengths, limitations, and trade-offs of each technique were discussed, and the possible future for these seven privacy enhancement techniques for healthcare FL systems was identified.

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Section: Mixed Hybrid and Miscellaneous Approachesmentioning

confidence: 99%

A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems

Gu,

Sabrina,

Fan

et al. 2023

IJERPH

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Federated Learning for Mobility Applications

Gecer,

Garbinato

2024

ACM Comput. Surv.

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The increasing concern for privacy and the use of machine learning on personal data led researchers to introduce new approaches to machine learning. Federated learning is one such a novel privacy-preserving machine-learning approach that “brings code to data”, unlike traditional machine-learning approaches that “bring data to code.” In addition to improving privacy, federated learning is beneficial for latency-sensitive mobility applications by providing local models. To the best of our knowledge, this paper is the first ever to survey mobility-related federated-learning solutions, such as traffic-flow prediction, next-location prediction, and point-of-interest recommendation. Our categorization is based on three main questions: Why use federated learning? to identify the motivation to use federated learning; What problems are being addressed? to examine problems that surface with federated learning and how they are solved; and How is federated learning implemented? to account for the solutions implemented by the authors surveyed. The selected papers are peer-reviewed and published in journals and conferences; they all adopt federated learning as their core approach. We introduce our conceptual model to characterize federated-learning solutions and to compare them. In our conceptual model, we define three abstract roles: data generator, learner, and aggregator. We also explain how the work in the selected papers fits into our conceptual model.

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Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Alotaibi,

Khan,

Mahmood

2024

Applied Sciences

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Federated learning has emerged as a promising approach for collaborative model training across distributed devices. Federated learning faces challenges such as Non-Independent and Identically Distributed (non-IID) data and communication challenges. This study aims to provide in-depth knowledge in the federated learning environment by identifying the most used techniques for overcoming non-IID data challenges and techniques that provide communication-efficient solutions in federated learning. The study highlights the most used non-IID data types, learning models, and datasets in federated learning. A systematic mapping study was performed using six digital libraries, and 193 studies were identified and analyzed after the inclusion and exclusion criteria were applied. We identified that enhancing the aggregation method and clustering are the most widely used techniques for non-IID data problems (used in 18% and 16% of the selected studies), and a quantization technique was the most common technique in studies that provide communication-efficient solutions in federated learning (used in 27% and 15% of the selected studies). Additionally, our work shows that label distribution skew is the most used case to simulate a non-IID environment, specifically, the quantity label imbalance. The supervised learning model CNN model is the most commonly used learning model, and the image datasets MNIST and Cifar-10 are the most widely used datasets when evaluating the proposed approaches. Furthermore, we believe the research community needs to consider the client’s limited resources and the importance of their updates when addressing non-IID and communication challenges to prevent the loss of valuable and unique information. The outcome of this systematic study will benefit federated learning users, researchers, and providers.

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CloudyFL

Cited by 4 publications

References 12 publications

A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems

A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems

Federated Learning for Mobility Applications

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

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