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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2901–2950 of 6771 papers

TitleStatusHype
Federated Learning with Bayesian Differential Privacy—0
Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study—0
Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation—0
Federated Learning with Buffered Asynchronous Aggregation—0
Federated Learning with Classifier Shift for Class Imbalance—0
Federated learning with class imbalance reduction—0
FEDERATED LEARNING FRAMEWORK BASED ON TRIMMED MEAN AGGREGATION RULES—0
Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning—0
Federated Learning with Compression: Unified Analysis and Sharp Guarantees—0
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes—0
Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks—0
Federated Learning with Data-Agnostic Distribution Fusion—0
Federated Learning with Decoupled Probabilistic-Weighted Gradient Aggregation—0
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping—0
Federated Learning with Differential Privacy—0
Federated Learning with Differential Privacy: An Utility-Enhanced Approach—0
Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges—0
Federated Learning with Discriminative Naive Bayes Classifier—0
Federated learning with distributed fixed design quantum chips and quantum channels—0
Federated Learning for Personalized Humor Recognition—0
Data-driven geophysics: from dictionary learning to deep learning—0
Federated Learning with Domain Shift Eraser—0
Federated Learning with Downlink Device Selection—0
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks—0
Federated Learning with Dynamic Client Arrival and Departure: Convergence and Rapid Adaptation via Initial Model Construction—0
Federated Learning with Dynamic Transformer for Text to Speech—0
Federated Learning for Water Consumption Forecasting in Smart Cities—0
Federated Learning with Erroneous Communication Links—0
Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting—0
Data-Driven Breakthroughs and Future Directions in AI Infrastructure: A Comprehensive Review—0
Federated Learning in Vehicular Networks—0
Federated Learning with Flexible Architectures—0
Federated Learning with Flexible Control—0
Federated Learning with GAN-based Data Synthesis for Non-IID Clients—0
Federated Learning for Ultra-Reliable Low-Latency V2V Communications—0
Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue—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
Data, Competition, and Digital Platforms—0
Federated learning with hierarchical clustering of local updates to improve training on non-IID data—0
Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecasting—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
Federated Learning with Instance-Dependent Noisy Label—0
Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance 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
Show:102550
← PrevPage 59 of 136Next →
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