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Showing 1 to 11 of 11 for “"Federated learning (Machine learning)"”.
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Advancing approaches to multi-target multi-camera tracking: Graph-based features, language integration, privacy preservation, and large language agent frameworks
… large-scale deployments, we develop FLaMMOn, a federated learning framework incorporating federated elastic weight consolidation (FedCurv) and federated representation learning (FedRep). FLaMMOn outperforms centralized approaches with an IDF1 score of 76.04% while ensuring robust privacy …
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Flexible and lightweight toolbox for federated learning on edge devices
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms
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Red de Federated Learning: de modelo cliente / servidor a P2P
… obtuvo un software que aplicaba el concepto de Federated Learning, esto es, la habilidad de hacer Machine Learning sin la necesidad de compartir ni centralizar los datos en una sola máquina. Este software seguía el diseño original propuesto por los ingenieros de Google, en el que hay un servidor …
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A Practical Approach to Federated Learning
Machine learning models benefit from large and diverse training datasets. However, it is difficult for an individual organization to collect sufficiently diverse data. Additionally, the sensitivity of the data and government regulations such as GDPR, HIPPA, and CCPA restrict how organizations can …
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Scaled: Scalable Federated Learning via Distributed Hash Table Based Overlays
… the private data in cloud centers for training Machine Learning (ML) models becomes unrealistic. To address this problem, Federated Learning (FL) is proposed. Yet, central bottleneck has become a severe concern since the central node in traditional FL is responsible for the communication and …
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Images in motion?: a first look into video leakage in federated learning
Federated learning (FL) allows multiple entities to train a shared model collaboratively. Its core, privacy-preserving principle is that participants only exchange model updates, such as gradients, and never their raw, sensitive data. This approach is fundamental for applications in domains where …
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Decentralized Aggregation Design and Study of Federated Learning
<p>The advent of machine learning techniques has given rise to modern devices with built-in models for decision making and providing rich content to users. This typically involves processing huge volumes of data in central servers and sending updated models to end-user devices. There are two main …
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Towards Intelligent Federated Learning Systems
… as GDPR, the privacy challenges of traditional machine learning have become more visible. Recent works leverage edge computing to preserve data privacy by keeping the data where it is (not shared during the training process), so-called "Edge Computing". In 2016, Google extended this idea to …
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Federated Linear Dimensionality Reduction
… and data-ownership, led to the creation of federated datasets. Such datasets are characterised by their massive size and are usually scattered across decentralised edge devices, each holding their local data samples. As exciting as these federated datasets might be, they introduce an …
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On Principles of Efficiency for Federated Learning
… edge computing technologies. In this landscape, Federated Learning (FL) has emerged as a crucial machine learning paradigm that enables collaborative model training across various institutions or devices, crucially preserving the privacy of raw and sensitive data. This approach is particularly …
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Reliable and decentralised deep learning for physiological data
… bodily functions and processes. By employing machine learning to model these data, especially with the advancement of mobile sensing technologies, it becomes feasible to automatically and continually monitor and diagnose one's health status. This holds considerable promise for easing the …