SOTAVerified

Federated Learning

Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.

This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.

Papers

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Showing 2951–3000 of 6771 papers

TitleStatusHype
Federated Learning with Limited Node Labels—0
Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication—0
Federated Learning with LoRA Optimized DeiT and Multiscale Patch Embedding for Secure Eye Disease Recognition—0
Federated Learning with Lossy Distributed Source Coding: Analysis and Optimization—0
Federated Learning with Manifold Regularization and Normalized Update Reaggregation—0
Federated Learning for UAV Swarms Under Class Imbalance and Power Consumption Constraints—0
Federated Learning for UAV-Based Spectrum Sensing: Enhancing Accuracy Through SNR-Weighted Model Aggregation—0
Federated Learning with Multi-resolution Model Broadcast—0
Data, Competition, and Digital Platforms—0
Federated Learning with Neural Graphical Models—0
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration—0
Adaptive Differential Privacy in Federated Learning: A Priority-Based Approach—0
Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis—0
2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection—0
Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation—0
Federated Multi-Task Learning for THz Wideband Channel and DoA Estimation—0
Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings—0
Federated Learning without Full Labels: A Survey—0
Federated Learning for the Classification of Tumor Infiltrating Lymphocytes—0
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach—0
Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks—0
Federated Learning with Personalization Layers—0
Federated Learning with Position-Aware Neurons—0
Data Assetization via Resources-decoupled Federated Learning—0
Federated Learning with Privacy-Preserving Ensemble Attention Distillation—0
Federated Learning with Projected Trajectory Regularization—0
Federated Learning With Quantized Global Model Updates—0
Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case—0
Federated Learning for Spoken Language Understanding—0
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies—0
Federated Learning with Regularized Client Participation—0
Federated Learning with Relative Fairness—0
Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology—0
Federated Learning for Sparse Principal Component Analysis—0
Federated Learning for Smart Healthcare: A Survey—0
Federated Learning with Server Learning: Enhancing Performance for Non-IID Data—0
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey—0
Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach—0
Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities—0
Federated Learning for Short Text Clustering—0
Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization—0
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach—0
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions—0
Federated Learning for Short-term Residential Load Forecasting—0
Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection—0
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization—0
Federated Linear Contextual Bandits with Heterogeneous Clients—0
Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages—0
Federated Learning for Secure and Efficient Device Activity Detection in mMTC Networks—0
Federated Learning for Resource-Constrained IoT Devices: Panoramas and State-of-the-art—0
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All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75—Unverified
2SiloBN + SAMmIoU49.1—Unverified
3SiloBNmIoU45.96—Unverified
4FedSAM + SWAmIoU43.42—Unverified
5FedASAM + SWAmIoU43.02—Unverified
6FedAvg + SWAmIoU42.48—Unverified
7FedASAMmIoU42.27—Unverified
8FedSAMmIoU41.22—Unverified
9FedAvgmIoU38.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32—Unverified
2FedSAM + SWAAcc@1-1262Clients68.12—Unverified
3FedAvg + SWAAcc@1-1262Clients67.52—Unverified
4FedASAMAcc@1-1262Clients64.23—Unverified
5FedSAMAcc@1-1262Clients63.72—Unverified
6FedAvgAcc@1-1262Clients61.91—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64—Unverified
2FedASAMACC@1-100Clients39.76—Unverified
3FedSAM + SWAACC@1-100Clients39.51—Unverified
4FedSAMACC@1-100Clients36.93—Unverified
5FedAvgACC@1-100Clients36.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62—Unverified
2FedASAMACC@1-100Clients40.81—Unverified
3FedSAM + SWAACC@1-100Clients39.24—Unverified
4FedAvgACC@1-100Clients38.59—Unverified
5FedSAMACC@1-100Clients38.56—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72—Unverified
2FedSAM + SWAACC@1-100Clients46.76—Unverified
3FedASAMACC@1-100Clients46.58—Unverified
4FedSAMACC@1-100Clients44.84—Unverified
5FedAvgACC@1-100Clients41.27—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27—Unverified
2FedASAMACC@1-100Clients47.78—Unverified
3FedSAM + SWAACC@1-100Clients46.47—Unverified
4FedSAMACC@1-100Clients46.05—Unverified
5FedAvgACC@1-100Clients42.17—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17—Unverified
2FedSAM + SWAACC@1-100Clients47.96—Unverified
3FedASAMACC@1-100Clients45.61—Unverified
4FedSAMACC@1-100Clients44.73—Unverified
5FedAvgACC@1-100Clients40.43—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01—Unverified
2FedSAM + SWAACC@1-100Clients39.3—Unverified
3FedASAMACC@1-100Clients36.04—Unverified
4FedSAMACC@1-100Clients31.04—Unverified
5FedAvgACC@1-100Clients30.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97—Unverified
2FedASAM + SWAACC@1-100Clients54.79—Unverified
3FedSAM + SWAACC@1-100Clients53.67—Unverified
4FedSAMACC@1-100Clients53.39—Unverified
5FedAvgACC@1-100Clients50.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5—Unverified
2FedSAM + SWAACC@1-100Clients54.36—Unverified
3FedASAM + SWAACC@1-100Clients54.1—Unverified
4FedSAMACC@1-100Clients53.97—Unverified
5FedAvgACC@1-100Clients50.66—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81—Unverified
2FedSAMACC@1-100Clients54.01—Unverified
3FedSAM + SWAACC@1-100Clients53.9—Unverified
4FedASAM + SWAACC@1-100Clients53.86—Unverified
5FedAvgACC@1-100Clients49.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2—Unverified