Application of machine learning optimization in cloud computing resource scheduling and management
Y Zhang, B Liu, Y Gong, J Huang, J Xu… - Proceedings of the 5th …, 2024 - dl.acm.org
In recent years, cloud computing has been widely used. Cloud computing refers to the
centralized computing resources, users through the access to the centralized resources to …
centralized computing resources, users through the access to the centralized resources to …
Reputation-aware federated learning client selection based on stochastic integer programming
Federated Learning (FL) has attracted wide research interest due to its potential in building
machine learning models while preserving users' data privacy. However, due to the …
machine learning models while preserving users' data privacy. However, due to the …
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset
Federated Learning (FL) is a distributed learning paradigm that can coordinate
heterogeneous edge devices to perform model training without sharing private data. While …
heterogeneous edge devices to perform model training without sharing private data. While …
Fair Concurrent Training of Multiple Models in Federated Learning
Federated learning (FL) enables collaborative learning across multiple clients. In most FL
work, all clients train a single learning task. However, the recent proliferation of FL …
work, all clients train a single learning task. However, the recent proliferation of FL …
Multi-Tier Client Selection for Mobile Federated Learning Networks
Federated learning (FL), which addresses data privacy issues by training models on
resource-constrained mobile devices in a distributed manner, has attracted significant …
resource-constrained mobile devices in a distributed manner, has attracted significant …
Reliable federated learning based on dual-reputation reverse auction mechanism in Internet of Things
Y Tang, Y Liang, Y Liu, J Zhang, L Ni, L Qi - Future Generation Computer …, 2024 - Elsevier
Federated learning, a promising distributed machine learning paradigm, has been used in
various Internet of Things (IoT) environments to solve isolated data island issues and protect …
various Internet of Things (IoT) environments to solve isolated data island issues and protect …
AdaCoOpt: Leverage the interplay of batch size and aggregation frequency for federated learning
Federated Learning (FL) is a distributed learning paradigm that can coordinate
heterogeneous edge devices to perform model training without sharing private raw data …
heterogeneous edge devices to perform model training without sharing private raw data …
Cache-Enabled Federated Learning Systems
Federated learning (FL) is a distributed paradigm for collaboratively learning models without
having clients disclose their private data. One natural and practically relevant metric to …
having clients disclose their private data. One natural and practically relevant metric to …
Client Recruitment for Federated Learning in ICU Length of Stay Prediction
V Scheltjens, LNW Momo, W Verbeke… - 2023 IEEE 19th …, 2023 - ieeexplore.ieee.org
Machine and deep learning methods for medical and healthcare applications have shown
significant progress and performance improvement in recent years. These methods require …
significant progress and performance improvement in recent years. These methods require …
SoK: Assessing the State of Applied Federated Machine Learning
T Müller, M Stäbler, H Gascón, F Köster… - arXiv preprint arXiv …, 2023 - arxiv.org
Machine Learning (ML) has shown significant potential in various applications; however, its
adoption in privacy-critical domains has been limited due to concerns about data privacy. A …
adoption in privacy-critical domains has been limited due to concerns about data privacy. A …