Search Results - "Federated learning"

The demand for food all over the world requires the implementation of advanced technologies to improve agricultural productivity. Federated Learning (FL) as a decentralized approach to machine learning facilitates collaborative model training on different data sources while maintaining privacy—makin...

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We propose Phase-Adaptive Federated Learning (PAFL), a novel framework for privacy-preserving personalized travel itinerary generation that dynamically balances privacy and utility through a phase-dependent aggregation mechanism inspired by phase-change materials. (1) PAFL’s primary objective is to...

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The field of Artificial Intelligence (AI) is rapidly evolving, creating a demand for sophisticated models that rely on substantial data and computational resources for training. However, the high costs associated with training these models have limited accessibility, leading to concerns about transp...

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As distributed sensing technologies evolve, the collection of time series data is becoming increasingly decentralized, which introduces serious challenges for both model training and data privacy protection. In response to this trend, federated time series anomaly detection enables collaborative ana...

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A federated learning (FL) framework for cloud–edge–client collaboration performs local aggregation of model parameters through edges, reducing communication overhead from clients to the cloud. This framework is particularly suitable for Internet of Things (IoT)-based secure computing scenarios that...

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Federated learning (FL) is a decentralized approach that aims to establish a global model by aggregating updates from diverse clients without sharing their local data. However, the approach becomes complicated when Byzantine clients join with arbitrary manipulation, referred to as malicious clients....

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Federated learning (FL) is a machine learning technique that distributes model training to multiple clients while allowing clients to keep their data local. Although the technique allows one to break free from data silos keeping data local, to coordinate such distributed training, it requires an orc...

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Federated learning (FL) is an advanced distributed machine learning method that effectively solves the data silo problem. With the increasing popularity of federated learning and the growing importance of privacy protection, federated learning methods that can securely aggregate models have received...

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The development of applications based on artificial intelligence and implemented over wireless networks is increasingly rapidly and is expected to grow dramatically in the future. The resulting demand for the aggregation of large amounts of data has caused serious communication bottlenecks in wirele...

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This paper proposes a simple yet unexplored measurement and federated learning system architecture for connected vehicles. The novelty of the introduced system is to combine the real-time data-sharing of crowdsensing with federated learning of global traffic models, providing up-to-date information...

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Abstract Machine learning model accuracy, generalization, and reliability are greatly affected by the training data quality. High-quality data-characterized by completeness, consistency, accuracy, representativeness and homogeneity enables meaningful pattern learning and robust prediction. In federa...

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Federated Learning (FL) is a decentralized machine learning (ML) approach where multiple clients collaboratively train a shared model over several update rounds without exchanging local data. Similar to centralized learning, determining hyperparameters (HPs) like learning rate and batch size remains...

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Modulation recognition technology, as one of the core technologies in the field of wireless communications, holds significant importance in intelligent communication systems such as link adaptation and IoT devices. In recent years, deep learning-based automatic modulation recognition (DL-AMR) has em...

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With the rapid development of mobile internet technology and increasing concerns over data privacy, Federated Learning (FL) has emerged as a significant framework for training machine learning models. Given the advancements in technology, User Equipment (UE) can now process multiple computing tasks...

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In federated learning, selecting participating devices (clients) is critical due to their inherent diversity. Clients typically hold non-IID data and possess varying computational and communication capabilities, which introduces heterogeneity that can impact overall system performance. Ignoring this...

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The increasing demand for distributed machine learning like Federated Learning (FL) in dynamic, resource-constrained edge environments, 5G/6G networks, and the proliferation of mobile and edge devices, presents significant challenges related to fault tolerance, elasticity, and communication efficien...

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The integration of smart city applications with healthcare has revolutionized patient monitoring and medical data management. However, ensuring the privacy and security of Electronic Health Records (EHR) remains a critical challenge, especially in IoT-based environments with resource-constrained dev...

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The Internet of Medical Things (IoMT) has significantly enhanced the healthcare sector by enabling advanced connectivity between smart medical devices, improving patient monitoring, and optimizing care quality. However, this increasing connectivity exposes these systems to cyberattacks that can comp...

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Federated learning (FL), which enables collaborative learning across distributed nodes, confronts a significant heterogeneity challenge, primarily including resource heterogeneity induced by different hardware platforms, and statistical heterogeneity originating from non-IID private data distributio...

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Internet of Things (IoT) and more new technologies have brought great convenience to daily life, but they have also raised more data security and privacy protection. To ensure the security of IoT data and user privacy in various scenarios, the federated learning (FL) method is optimized based on fun...

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