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Found 19 papers, 4 papers with code
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning
Personalized FL (PFL) addresses this by synthesizing personalized models from a global model via training on local data.
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
It is specifically designed for code-related tasks with both English and Chinese prompts and supports over 40 programming languages.
Federated cINN Clustering for Accurate Clustered Federated Learning
However, a significant challenge arises when coordinating FL with crowd intelligence which diverse client groups possess disparate objectives due to data heterogeneity or distinct tasks.
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence
Reducing communication overhead in federated learning (FL) is challenging but crucial for large-scale distributed privacy-preserving machine learning.
LoS sensing-based superimposed CSI feedback for UAV-Assisted mmWave systems
Specifically, for the G2U CSI in NLoS, a CSI recovery network (CSI-RecNet) and superimposed interference cancellation are developed to recover the G2U CSI and U2G data.
Personalized Federated Learning with Hidden Information on Personalized Prior
Federated learning (FL for simplification) is a distributed machine learning technique that utilizes global servers and collaborative clients to achieve privacy-preserving global model training without direct data sharing.
Ranked #1 on
Image Classification
on Fashion-MNIST
(Accuracy metric)
DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg
Federated learning (FL) is identified as a crucial enabler for large-scale distributed machine learning (ML) without the need for local raw dataset sharing, substantially reducing privacy concerns and alleviating the isolated data problem.
Deep Learning for 1-Bit Compressed Sensing-based Superimposed CSI Feedback
In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, 1-bit compressed sensing (CS)-based superimposed channel state information (CSI) feedback has shown many advantages, while still faces many challenges, such as low accuracy of the downlink CSI recovery and large processing delays.
Fusion Learning for 1-Bit CS-based Superimposed CSI Feedback with Bi-Directional Channel Reciprocity
Due to the discarding of downlink channel state information (CSI) amplitude and the employing of iteration reconstruction algorithms, 1-bit compressed sensing (CS)-based superimposed CSI feedback is challenged by low recovery accuracy and large processing delay.
Label Design-based ELM Network for Timing Synchronization in OFDM Systems with Nonlinear Distortion
Due to the nonlinear distortion in Orthogonal frequency division multiplexing (OFDM) systems, the timing synchronization (TS) performance is inevitably degraded at the receiver.
MRC-LSTM: A Hybrid Approach of Multi-scale Residual CNN and LSTM to Predict Bitcoin Price
Bitcoin, one of the major cryptocurrencies, presents great opportunities and challenges with its tremendous potential returns accompanying high risks.
Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search
In this paper, we propose a novel approach, Heart-Darts, to efficiently classify the ECG signals by automatically designing the CNN model with the differentiable architecture search (i. e., Darts, a cell-based neural architecture search method).
LANA: Towards Personalized Deep Knowledge Tracing Through Distinguishable Interactive Sequences
With pivot module reconstructed the decoder for individual students and leveled learning specialized encoders for groups, personalized DKT was achieved.
Unbiased Subdata Selection for Fair Classification: A Unified Framework and Scalable Algorithms
We prove that in the proposed framework, when the classification outcomes are known, the resulting problem, termed "unbiased subdata selection," is strongly polynomial-solvable and can be used to enhance the classification fairness by selecting more representative data points.
PSO-PS: Parameter Synchronization with Particle Swarm Optimization for Distributed Training of Deep Neural Networks
Synchronous methods are widely used in distributed training the Deep Neural Networks (DNNs).
HPSGD: Hierarchical Parallel SGD With Stale Gradients Featuring
While distributed training significantly speeds up the training process of the deep neural network (DNN), the utilization of the cluster is relatively low due to the time-consuming data synchronizing between workers.
DBS: Dynamic Batch Size For Distributed Deep Neural Network Training
Specifically, the performance of each worker is evaluatedfirst based on the fact in the previous epoch, and then the batch size and datasetpartition are dynamically adjusted in consideration of the current performanceof the worker, thereby improving the utilization of the cluster.
Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors
The proposed network, referred to as SRSCN, comprises a shape reconstruction neural network (SRNN) and a spatial constraint network (SCN).
InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset
Datasets have gained an enormous amount of popularity in the computer vision community, from training and evaluation of Deep Learning-based methods to benchmarking Simultaneous Localization and Mapping (SLAM).
