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Federated unsupervised representation learning
To leverage the enormous amount of unlabeled data on distributed edge devices, we formulate a new problem in federated learning called federated...
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Blind Federated Learning without initial model
Federated learning is an emerging machine learning approach that allows the construction of a model between several participants who hold their own...
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A survey on federated learning: a perspective from multi-party computation
Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their...
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PPFLV: privacy-preserving federated learning with verifiability
Federated learning, as an emerging framework for distributed machine learning, has received widespread attention. In federated learning, the cloud...
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FedEem: a fairness-based asynchronous federated learning mechanism
Federated learning is a mechanism for model training in distributed systems, aiming to protect data privacy while achieving collective intelligence....
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Federated learning analysis for vehicular traffic flow prediction: evaluation of learning algorithms and aggregation approaches
The increasing development and implementation of Intelligent Transportation System have led to a growing focus on traffic flow prediction. To make...
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Data-free adaptive structured pruning for federated learning
Federated learning faces challenges in real-world deployment scenarios due to limited client resources and the problem of stragglers caused by high...
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Transforming educational insights: strategic integration of federated learning for enhanced prediction of student learning outcomes
Numerous educational institutions utilize data mining techniques to manage student records, particularly those related to academic achievements,...
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Cluster knowledge-driven vertical federated learning
In industrial scenarios, cross-departmental collaboration is necessary to achieve quality traceability. However, data cannot be shared due to privacy...
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Exploring the distributed learning on federated learning and cluster computing via convolutional neural networks
Distributed learning has led to the development of federated learning and cluster computing; however, the two methods are very different. Therefore,...
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Overhead-free Noise-tolerant Federated Learning: A New Baseline
Federated learning (FL) is a promising decentralized machine learning approach that enables multiple distributed clients to train a model jointly...
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Towards Long-Term Remembering in Federated Continual Learning
BackgroundFederated Continual Learning (FCL) involves learning from distributed data on edge devices with incremental knowledge. However, current FCL...
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Layer-Wise Personalized Federated Learning with Hypernetwork
Federated learning is a machine learning paradigm in which decentralized client devices collaboratively train shared model under the coordinating of...
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Privacy-preserving federated learning based on partial low-quality data
Traditional machine learning requires collecting data from participants for training, which may lead to malicious acquisition of privacy in...
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Federated learning: a comprehensive review of recent advances and applications
Federated Learning is a promising technique for preserving data privacy that enables communication between distributed nodes without the need for a...
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A review of federated learning: taxonomy, privacy and future directions
The data generated and stored in mobile devices owned by individuals as well as in various organizations contains a large amount of valuable and...
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eXtreme Federated Learning (XFL): a layer-wise approach
Federated learning (FL) is a machine learning technique that builds models by using distributed data across devices. FL aggregates parameter updates...
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Communication efficiency optimization of federated learning for computing and network convergence of 6G networks
Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global models. However,...
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Communication-efficient federated continual learning for distributed learning system with Non-IID data
Due to the privacy preserving capabilities and the low communication costs, federated learning has emerged as an efficient technique for distributed...
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Eye Disease Detection Through Image Classification Using Federated Learning
In recent years, the fields of artificial intelligence and deep learning have had a significant impact on the field of ocular imaging. One area where...
