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Found 16 papers, 3 papers with code
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study
The innovative Federated Multi-Task Learning (FMTL) approach consolidates the benefits of Federated Learning (FL) and Multi-Task Learning (MTL), enabling collaborative model training on multi-task learning datasets.
FedHCA$^2$: Towards Hetero-Client Federated Multi-Task Learning
Moreover, we employ learnable Hyper Aggregation Weights for each client to customize personalized parameter updates.
UNIDEAL: Curriculum Knowledge Distillation Federated Learning
Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients while preserving data privacy.
Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction
In this paper, we propose an environment semantics aided wireless communication framework to reduce the transmission latency and improve the transmission reliability, where semantic information is extracted from environment image data, selectively encoded based on its task-relevance, and then fused to make decisions for channel related tasks.
EDEN: A Plug-in Equivariant Distance Encoding to Beyond the 1-WL Test
While most existing message-passing graph neural networks (MPNNs) are permutation-invariant in graph-level representation learning and permutation-equivariant in node- and edge-level representation learning, their expressive power is commonly limited by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test.
Position-Aware Subgraph Neural Networks with Data-Efficient Learning
2) Prevailing graph augmentation methods for GEL, including rule-based, sample-based, adaptive, and automated methods, are not suitable for augmenting subgraphs because a subgraph contains fewer nodes but richer information such as position, neighbor, and structure.
Completely Heterogeneous Federated Learning
Federated learning (FL) faces three major difficulties: cross-domain, heterogeneous models, and non-i. i. d.
NoMorelization: Building Normalizer-Free Models from a Sample's Perspective
The normalizing layer has become one of the basic configurations of deep learning models, but it still suffers from computational inefficiency, interpretability difficulties, and low generality.
Federated Dynamic Neural Network for Deep MIMO Detection
In this paper, we develop a dynamic detection network (DDNet) based detector for multiple-input multiple-output (MIMO) systems.
Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming
In this paper, we propose a novel deep unsupervised learning-based approach that jointly optimizes antenna selection and hybrid beamforming to improve the hardware and spectral efficiencies of massive multiple-input-multiple-output (MIMO) downlink systems.
FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots
In this paper, we propose a new downlink beamforming strategy for mmWave communications using uplink sub-6GHz channel information and a very few mmWave pilots.
Deep Multimodal Learning: Merging Sensory Data for Massive MIMO Channel Prediction
However, the research on MSI aided intelligent communications has not yet explored how to integrate and fuse the multimodal sensory data, which motivates us to develop a systematic framework for wireless communications based on deep multimodal learning (DML).
Learning Combined Set Covering and Traveling Salesman Problem
The Traveling Salesman Problem is one of the most intensively studied combinatorial optimization problems due both to its range of real-world applications and its computational complexity.
Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems
Specifically, we develop the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained on the data from all previous environments in the manner of classical deep learning and is then fine-tuned for new environments.
Model-aided Deep Neural Network for Source Number Detection
Specifically, we first propose the eigenvalue based regression network (ERNet) and classification network (ECNet) to estimate the number of non-coherent sources, where the eigenvalues of the received signal covariance matrix and the source number are used as the input and the supervise label of the networks, respectively.
Optimizing vaccine distribution networks in low and middle-income countries
Vaccination has been proven to be the most effective method to prevent infectious diseases.
