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 18011850 of 6771 papers

TitleStatusHype
FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness ReweightingCode0
FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of VehiclesCode2
Towards Federated Domain Unlearning: Verification Methodologies and Challenges0
Optimal Rates for O(1)-Smooth DP-SCO with a Single Epoch and Large Batches0
Parameterizing Federated Continual Learning for Reproducible Research0
Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregation0
Federated Class-Incremental Learning with Hierarchical Generative Prototypes0
Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-MultinomialsCode3
PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly DetectionCode1
A Comparative Study of Sampling Methods with Cross-Validation in the FedHome Framework0
One-Shot Federated Learning with Bayesian Pseudocoresets0
FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning0
Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning0
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingCode1
No Vandalism: Privacy-Preserving and Byzantine-Robust Federated Learning0
Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems0
FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation0
Efficient Data Distribution Estimation for Accelerated Federated Learning0
Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients0
Asynchronous Byzantine Federated Learning0
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers0
A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with AutoencoderCode0
Local Methods with Adaptivity via Scaling0
SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational OverheadCode0
FedAST: Federated Asynchronous Simultaneous Training0
Federated Model Heterogeneous Matryoshka Representation Learning0
Redefining Contributions: Shapley-Driven Federated LearningCode1
Non-Federated Multi-Task Split Learning for Heterogeneous Sources0
Sheaf HyperNetworks for Personalized Federated Learning0
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning0
Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learning0
Share Secrets for Privacy: Confidential Forecasting with Vertical Federated LearningCode0
GI-NAS: Boosting Gradient Inversion Attacks through Adaptive Neural Architecture Search0
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning0
Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity0
Pursuing Overall Welfare in Federated Learning through Sequential Decision MakingCode1
SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning0
Gradient Inversion of Federated Diffusion Models0
On Vessel Location Forecasting and the Effect of Federated LearningCode0
subMFL: Compatiple subModel Generation for Federated Learning in Device Heterogenous EnvironmentCode0
Federated Learning with Multi-resolution Model Broadcast0
Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting0
Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective0
Federated and Transfer Learning for Cancer Detection Based on Image Analysis0
Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning0
Block-FeDL: Electric Vehicle Charging Load Forecasting using Federated Learning and Blockchain0
LoByITFL: Low Communication Secure and Private Federated Learning0
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware MinimizationCode1
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP OptimizationCode0
Optimizing Split Points for Error-Resilient SplitFed Learning0
Show:102550
← PrevPage 37 of 136Next →

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