首页|New Findings from QILU University of Technology Describe Advances in Machine Learning (Asynchronous Federated Learning On Heterogeneous Devices: a Survey)
New Findings from QILU University of Technology Describe Advances in Machine Learning (Asynchronous Federated Learning On Heterogeneous Devices: a Survey)
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NETL
NSTL
Elsevier
A new study on Machine Learning is now available. According to news reporting originating from Jinan, People's Republic of China, by NewsRx correspondents, research stated, "Federated learning (FL) is a kind of distributed machine learning framework, where the global model is generated on the centralized aggregation server based on the parameters of local models, addressing concerns about privacy leakage caused by the collection of local training data. With the growing computational and communication capacities of edge and IoT devices, applying FL on heterogeneous devices to train machine learning models is becoming a prevailing trend." Financial supporters for this research include National Key R&D Program of China, Australian Research Council.
JinanPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningQILU University of Technology
NETL
NSTL
Elsevier
