Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

Patel, Vishwa Amitkumar; Bhattacharya, Pronaya; Tanwar, Sudeep; Gupta, Rajesh; Bokoro, Pitshou N.; Sharma, Ravi

doi:10.1109/access.2022.3201876

Cited by 32 publications

(18 citation statements)

References 113 publications

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“…Replace ← −−−−− − bmi(mean)s.t., (8) bmi(null) and bmi(mean) ∈ D, (9) where D represents the entire dataset.…”

Section: Bmi(null)mentioning

confidence: 99%

“…The clients share localized model data that is aggregated at the central server. This aggregation allows the central server to use the statistical information of several models and compute improved values which are then shared back with the clients 9 . Hence, the clients obtain an improved model that can perform on their local data as well as data following the distributions of other clients without having access to the actual data of those clients.…”

Section: Introductionmentioning

confidence: 99%

“…This aggregation allows the central server to use the statistical information of several models and compute improved values which are then shared back with the clients. 9 Hence, the clients obtain an improved model that can perform on their local data as well as data following the distributions of other clients without having access to the actual data of those clients. FL-based systems are also capable of adjusting the learning process to accommodate for differing levels of performances of the clients involved in the aggregation.…”

mentioning

confidence: 99%

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Artificial neural network‐driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

Bhatt,

Jadav,

Kumari

et al. 2023

Concurrency and Computation

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SummaryIn recent years, smart healthcare, artificial intelligence (AI)‐aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL‐based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre‐established datasets that fail to capture real‐life applicability. Therefore, this study proposes an AI‐enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare‐based wearable devices (WD) for real‐time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL‐based architecture aggregates the optimizer weights of many clients using a fifth‐generation (5G) communication channel. Then, the performance of the proposed FL‐based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher.

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“…Replace ← −−−−− − bmi(mean)s.t., (8) bmi(null) and bmi(mean) ∈ D, (9) where D represents the entire dataset.…”

Section: Bmi(null)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Artificial neural network‐driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

Bhatt,

Jadav,

Kumari

et al. 2023

Concurrency and Computation

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“…To represent the benefits of GeFL, we simulated classifying traffic signs using a convolutional neural network (CNN) and federated learning (FL) without violating privacy. It is based on gradient encryption to improve the security and privacy of federated (distributed) learning algorithms [10]. As discussed above, many modules in AVs work simultaneously for the efficient driving task.…”

Section: Introductionmentioning

confidence: 99%

GeFL: Gradient Encryption-Aided Privacy Preserved Federated Learning for Autonomous Vehicles

et al. 2023

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Autonomous vehicles (AVs) are getting popular because of their usage in a wide range of applications like delivery systems, self-driving taxis, and ambulances. AVs utilize the power of machine learning (ML) and deep learning (DL) algorithms to improve their self-driving learning experiences. The sudden surge in the number of AVs raises the need for distributed learning ecosystem to optimize their self-driving experiences at a rapid pace. Toward this goal, federated learning (FL) benefits, which can create a distributed learning environment for AVs. But, the traditional FL transfers the raw input data directly to a server, which leads to privacy concerns among the end-users. The concept of blockchain helps us to protect privacy, but it requires additional computational infrastructure. The extra infrastructure increases the operational cost for the company handling and maintaining the AVs. Motivated by this, in this paper, the authors introduced the concept of gradient encryption in FL, which preserves the users' privacy without the additional computation requirements. The computational power present in the edge devices helps to fine-tune the local model and encrypt the input data to preserve privacy without any drop in performance. For performance evaluation, the authors have built a German traffic sign recognition system using a convolutional neural network (CNN) algorithm-based classification system and GeFL. The simulation process is carried out over a wide range of input parameters to analyze the performance at scale. Simulation results of GeFL outperform the conventional FL-based algorithms in terms of accuracy, i.e., 2% higher. Also, the amount of data transferred among the devices in the network is nearly three times less in GeFL compared to the traditional FL.

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“…The GE data are trained locally (with the GP's avatar and personalization data) and are sent to the global model as an update. Coupled with BC, trusted FL training is possible, as local updates are verified as transactional ledgers [13,14].…”

Section: Introductionmentioning

confidence: 99%

Game-o-Meta: Trusted Federated Learning Scheme for P2P Gaming Metaverse beyond 5G Networks

Bhattacharya

Verma

Prasad

et al. 2023

Sensors

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The aim of the peer-to-peer (P2P) decentralized gaming industry has shifted towards realistic gaming environment (GE) support for game players (GPs). Recent innovations in the metaverse have motivated the gaming industry to look beyond augmented reality and virtual reality engines, which improve the reality of virtual game worlds. In gaming metaverses (GMs), GPs can play, socialize, and trade virtual objects in the GE. On game servers (GSs), the collected GM data are analyzed by artificial intelligence models to personalize the GE according to the GP. However, communication with GSs suffers from high-end latency, bandwidth concerns, and issues regarding the security and privacy of GP data, which pose a severe threat to the emerging GM landscape. Thus, we proposed a scheme, Game-o-Meta, that integrates federated learning in the GE, with GP data being trained on local devices only. We envisioned the GE over a sixth-generation tactile internet service to address the bandwidth and latency issues and assure real-time haptic control. In the GM, the GP’s game tasks are collected and trained on the GS, and then a pre-trained model is downloaded by the GP, which is trained using local data. The proposed scheme was compared against traditional schemes based on parameters such as GP task offloading, GP avatar rendering latency, and GS availability. The results indicated the viability of the proposed scheme.

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Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

Cited by 32 publications

References 113 publications

Artificial neural network‐driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

Artificial neural network‐driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

GeFL: Gradient Encryption-Aided Privacy Preserved Federated Learning for Autonomous Vehicles

Game-o-Meta: Trusted Federated Learning Scheme for P2P Gaming Metaverse beyond 5G Networks

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