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

Showing 25012550 of 6771 papers

TitleStatusHype
Federated Deep Subspace Clustering0
FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networks0
Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data0
Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL0
Federated Distillation: A Survey0
Federated Distillation based Indoor Localization for IoT Networks0
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis0
Connecting Federated ADMM to Bayes0
Federated Domain Adaptation for ASR with Full Self-Supervision0
Considerations on the Theory of Training Models with Differential Privacy0
Federated Domain Generalization: A Survey0
Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth0
Federated Domain Generalization with Data-free On-server Matching Gradient0
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training0
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data0
Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks0
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data0
Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices0
Federated Dropout: Convergence Analysis and Resource Allocation0
Federated Dropout Learning for Hybrid Beamforming With Spatial Path Index Modulation In Multi-User mmWave-MIMO Systems0
Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees0
Federated Dynamic GNN with Secure Aggregation0
Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting0
A Secure Federated Learning Framework for 5G Networks0
Federated Dynamic Spectrum Access0
Federated Edge Learning : Design Issues and Challenges0
Federated Learning-based Active Authentication on Mobile Devices0
Federated-EM with heterogeneity mitigation and variance reduction0
Federated EndoViT: Pretraining Vision Transformers via Federated Learning on Endoscopic Image Collections0
FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration0
Federated Ensemble Model-based Reinforcement Learning in Edge Computing0
Federated Ensemble YOLOv5 -- A Better Generalized Object Detection Algorithm0
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications0
Federated Expectation Maximization with heterogeneity mitigation and variance reduction0
Federated Face Presentation Attack Detection0
Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification0
Federated Face Recognition0
Continual Learning for Smart City: A Survey0
Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDM0
Federated Fairness without Access to Sensitive Groups0
FedDA-TSformer: Federated Domain Adaptation with Vision TimeSformer for Left Ventricle Segmentation on Gated Myocardial Perfusion SPECT Image0
Continuous-Time Analysis of Federated Averaging0
A Secure Federated Learning Framework for Residential Short Term Load Forecasting0
Federated Fine-Tuning for Pre-Trained Foundation Models Over Wireless Networks0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation0
Federated Fine-Tuning of Foundation Models via Probabilistic Masking0
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources0
Federated Fine-Tuning of Large Language Models: Kahneman-Tversky vs. Direct Preference Optimization0
Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions0
Fed-DART and FACT: A solution for Federated Learning in a production environment0
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Benchmark Results

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