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Found 5 papers, 1 papers with code
An Innovative Networks in Federated Learning
This paper presents the development and application of Wavelet Kolmogorov-Arnold Networks (Wav-KAN) in federated learning.
Clipped Uniform Quantizers for Communication-Efficient Federated Learning
This paper introduces an approach to employ clipped uniform quantization in federated learning settings, aiming to enhance model efficiency by reducing communication overhead without compromising accuracy.
Wav-KAN: Wavelet Kolmogorov-Arnold Networks
In this paper, we introduce Wav-KAN, an innovative neural network architecture that leverages the Wavelet Kolmogorov-Arnold Networks (Wav-KAN) framework to enhance interpretability and performance.
Auto-Calibration and 2D-DOA Estimation in UCAs via an Integrated Wideband Dictionary
In this paper, we present a novel auto-calibration scheme for the joint estimation of the two-dimensional (2-D) direction-of-arrival (DOA) and the mutual coupling matrix (MCM) for a signal measured using uniform circular arrays.
Real-Valued 2-D Direction of Arrival Estimation via Sparse Representation
Despite many advantages of direction-of-arrivals (DOAs) in sparse representation domain, they have high computational complexity.
