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Found 10 papers, 1 papers with code
Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices
Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters significant challenges due to the data and resource heterogeneity of edge devices.
Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism
This allows WIoU to focus on ordinary-quality anchor boxes and improve the detector's overall performance.
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices
We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints.
FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing
Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without uploading the local data.
To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices
Federated learning (FL), an emerging distributed machine learning paradigm, in conflux with edge computing is a promising area with novel applications over mobile edge devices.
FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing
In PVEC, different PLOs recruit PVs as edge computing nodes for offloading services through an incentive mechanism, which is designed according to the computation demand and parking capacity constraints derived from FedParking.
Enhancement of Superconductivity Linked with Linear-in-Temperature/Field Resistivity in Ion-Gated FeSe Films
Iron selenide (FeSe) - the structurally simplest iron-based superconductor, has attracted tremendous interest in the past years.
Superconductivity
Orbital-selective correlations and renormalized electronic structure in LiFeAs
Multiorbital models are important to both the correlation physics and topological behavior of quantum materials.
Superconductivity Strongly Correlated Electrons
Evaluation of Inference Attack Models for Deep Learning on Medical Data
This paper aims to attract interest from researchers in the medical deep learning community to this important problem.
Differentially Private and Fair Classification via Calibrated Functional Mechanism
In order to enforce $\epsilon$-differential privacy and fairness, we leverage the functional mechanism to add different amounts of Laplace noise regarding different attributes to the polynomial coefficients of the objective function in consideration of fairness constraint.
