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2003.04544
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Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning
IEEE Transactions on Wireless Communications (TWC), 2020
10 March 2020
Dingzhu Wen
M. Bennis
Kaibin Huang
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Papers citing
"Joint Parameter-and-Bandwidth Allocation for Improving the Efficiency of Partitioned Edge Learning"
33 / 33 papers shown
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Task-Oriented Over-the-Air Computation for Multi-Device Edge AI
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Trustworthy Federated Learning via Blockchain
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Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications
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Fast Convergence Algorithm for Analog Federated Learning
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Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels
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Energy Efficient Federated Learning Over Wireless Communication Networks
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Device Scheduling with Fast Convergence for Wireless Federated Learning
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A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
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Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air
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Deniz Gunduz
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Federated Learning via Over-the-Air Computation
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Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)
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Towards an Intelligent Edge: Wireless Communication Meets Machine Learning
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Dongzhu Liu
Yuqing Du
Changsheng You
Jun Zhang
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245
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Efficient Decentralized Deep Learning by Dynamic Model Averaging
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Joachim Sicking
Fabian Hüger
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LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
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Sparse Communication for Distributed Gradient Descent
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R. Monga
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Distributed optimization of deeply nested systems
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