Energy Efficient Federated Learning
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Energy Efficient Federated Learning with Hyperdimensional Computing over Wireless Communication Networks
arXiv:2602.21949v2 Announce Type: replace Abstract: In this paper, we investigate a problem of minimizing total energy consumption for secure federated learning (FL) over wireless edge networks. To address the high computational cost and privacy challenges in conventional FL with neural networks (NN) for resource-constrained users, we propose a novel FL with hyperdimensional computing and differential privacy (FL-HDC-DP) framework. In the considered model, each edge user employs...
Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Design
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A 65-nm Privacy-Preserving Neuromorphic Encoder With 7.13-nJ Efficiency, 2.38-Mb/mm^2 Item-Memory Density, and Federated Learning Support
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Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks
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AI is blowing up music. How should the Grammys handle it?
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