Federated learning review: Fundamentals, enabling technologies, and future applications

Banabilah, Syreen; Aloqaily, Moayad; Alsayed, Eitaa; Malik, Nida; Jararweh, Yaser

doi:10.1016/j.ipm.2022.103061

Cited by 168 publications

(32 citation statements)

References 82 publications

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“…The FL model for IoT-enabled healthcare delivery provided several solutions for interconnected IoT devices’ diverse, low-power, and distributed nature. The ear- lier research discusses the architecture of federated learning, system problems, and a method for protecting privacy [ 9 ]. With the advancement of wearables and sensor technologies, enormous data is now evolving, which is essential to be trained using different ML algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…With the advancement of wearables and sensor technologies, enormous data is now evolving, which is essential to be trained using different ML algorithms. Syreen et al [ 9 ] proposed several clas- sification and clustering techniques for FL-enabled IoT systems. The resource- constraint nature of IoT makes these devices share data with edge nodes using different communication protocols, which are prone to cyber-attacks.…”

Section: Related Workmentioning

confidence: 99%

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A federated learning approach for smart healthcare systems

Mishra

Saha

Mishra³

et al. 2023

CSIT

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With periodic technology advancements and pandemic-like situations, remote patient health monitoring has increased significantly. The Internet of Things (IoT) devices, including wearables, sensors, and actuators deployed on the human body, detect and regulate physiological data. These systems can establish a trigger mechanism in the event of a possible health incident. Health monitoring using IoT devices generates a large amount of data. Several Machine Learning (ML) strategies have been utilized to analyze the collected data and derive precise predictions. The confidentiality of patient data is one of the essential requirements of these systems. It has been discovered that malicious coordination of ML algorithms might result in a massive attack surface, providing cyber- criminals with an accessible platform. Considering these requirements for using IoT systems for health monitoring, Federated Learning (FL) can solve data privacy challenges by training ML models locally on IoT de- vices without any data transfer to the cloud. FL facilitates information sharing among all IoT devices installed in hospitals through collabora- tive ML model training. This article will examine the significance of em- bedding FL into IoT-enabled smart hospitals and future guidelines for accomplishing this. This article discusses identifying rare diseases and critical care, resolving the problem of insufficient patient health data to train ML models, and preserving patient privacy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A federated learning approach for smart healthcare systems

Mishra

Saha

Mishra³

et al. 2023

CSIT

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“…Data privacy is assured by training a prediction model through decentralized data, locally associated with different clients and not exchanged or transferred. Federated Learning is applied to support privacy-sensitive applications in several fields [ 24 ].…”

Section: State Of the Artmentioning

confidence: 99%

“…The framework is applicable in different domains [ 23 , 24 ]: healthcare, bank loans, advertising, financial fraud, and insurance, among others. In this paper, we focus our attention on the medical field, where there is a high need for data privacy and interpretability of the solutions.…”

Section: Introductionmentioning

confidence: 99%

A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction

Falco

Cioppa

Koutný

et al. 2023

Sensors

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In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the literature, ours can efficiently deal at the same time with two relevant issues in Machine Learning, i.e., data privacy and interpretability of the solutions. Our framework consists of a master/slave approach in which each slave contains local data, protecting sensible private data, and exploits an evolutionary algorithm to generate prediction models. The master shares through the slaves the locally learned models that emerge on each slave. Sharing these local models results in global models. Being that data privacy and interpretability are very significant in the medical domain, the algorithm is tested to forecast future glucose values for diabetic patients by exploiting a Grammatical Evolution algorithm. The effectiveness of this knowledge-sharing process is assessed experimentally by comparing the proposed framework with another where no exchange of local models occurs. The results show that the performance of the proposed approach is better and demonstrate the validity of its sharing process for the emergence of local models for personal diabetes management, usable as efficient global models. When further subjects not involved in the learning process are considered, the models discovered by our framework show higher generalization capability than those achieved without knowledge sharing: the improvement provided by knowledge sharing is equal to about 3.03% for precision, 1.56% for recall, 3.17% for F1, and 1.56% for accuracy. Moreover, statistical analysis reveals the statistical superiority of model exchange with respect to the case of no exchange taking place.

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“…Privacy, security and communication threats offered by traditional ML models have been automatically eliminated as data is kept safe with the owner organization. Moreover, network issues have also been suppressed as the huge dataset is never being transferred over the network rather a small sized model trained from local data is travelling over the network reducing overheads [24]. Privacy and data security issues related to traditional ML based solutions have enforced the application of FL technique for the training of secured and privacy protected flood prediction models.…”

Section: Introductionmentioning

confidence: 99%

FFM: Flood Forecasting Model Using Federated Learning

et al. 2023

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Floods are one of the most common natural disasters that occur frequently causing massive damage to property, agriculture, economy and life. Flood prediction offers a huge challenge for researchers struggling to predict floods since long time. In this article, flood forecasting model using federated learning technique has been proposed. Federated Learning is the most advanced technique of machine learning (ML) that guarantees data privacy, ensures data availability, promises data security, and handles network latency trials inherent in prediction of floods by prohibiting data to be transferred over the network for model training. Federated Learning technique urges for onsite training of local data models, and focuses on transmission of these local models on the network instead of sending huge data set towards central server for local model aggregation and training of global data model at the central server. In this article, the proposed model integrates locally trained models of eighteen clients, investigates at which station flooding is about to happen and generates flood alert towards a specific client with five days lead time. A local feed forward neural network (FFNN) model is trained at the client station where the flood has been expected. Flood forecasting module of local FFNN model predicts the expected water level by taking multiple regional parameters as input. The dataset of five different rivers and barrages has been collected from 2015 to 2021 considering four aspects including snow melting, rainfall-runoff, flow routing and hydrodynamics. The proposed flood forecasting model has successfully predicted previous floods happened in the selected zone during 2010 to 2015 with 84 % accuracy. INDEX TERMS hydraulic, meteorological, flood forecasting system, federated learning, feed-forward neural network.FIGURE 1. Natural hazards percentage as per occurrence in Pakistan during 2021 [6]

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Federated learning review: Fundamentals, enabling technologies, and future applications

Cited by 168 publications

References 82 publications

A federated learning approach for smart healthcare systems

A federated learning approach for smart healthcare systems

A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction

FFM: Flood Forecasting Model Using Federated Learning

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