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 58515900 of 6771 papers

TitleStatusHype
A Potential Game Perspective in Federated LearningCode0
Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client AvailabilityCode0
FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated LearningCode0
Label Inference Attack against Split Learning under Regression SettingCode0
FedPH: Privacy-enhanced Heterogeneous Federated LearningCode0
FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile DevicesCode0
FedCAP: Robust Federated Learning via Customized Aggregation and PersonalizationCode0
Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA AllocationCode0
FedHe: Heterogeneous Models and Communication-Efficient Federated LearningCode0
FedHarmony: Unlearning Scanner Bias with Distributed DataCode0
FedGS: Federated Gradient Scaling for Heterogeneous Medical Image SegmentationCode0
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients InspectionCode0
FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMsCode0
Distributed Collapsed Gibbs Sampler for Dirichlet Process Mixture Models in Federated LearningCode0
FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated LearningCode0
Label Leakage in Federated Inertial-based Human Activity RecognitionCode0
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss ApproximationsCode0
Label Privacy in Split Learning for Large Models with Parameter-Efficient TrainingCode0
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated LearningCode0
Attentive Federated Learning for Concept Drift in Distributed 5G Edge NetworksCode0
"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated LearningCode0
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID DataCode0
Safe-EF: Error Feedback for Nonsmooth Constrained OptimizationCode0
FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated LearningCode0
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical ImagingCode0
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET DenoisingCode0
Private Multi-Task Learning: Formulation and Applications to Federated LearningCode0
Private Non-Convex Federated Learning Without a Trusted ServerCode0
LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed LearningCode0
FedMultimodal: A Benchmark For Multimodal Federated LearningCode0
FedFT: Improving Communication Performance for Federated Learning with Frequency Space TransformationCode0
FedCVT: Semi-supervised Vertical Federated Learning with Cross-view TrainingCode0
Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without ReplacementCode0
FedFOR: Stateless Heterogeneous Federated Learning with First-Order RegularizationCode0
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion TheoryCode0
Optimizing the Numbers of Queries and Replies in Federated Learning with Differential PrivacyCode0
Latency Optimization for Wireless Federated Learning in Multihop NetworksCode0
A Novel Optimized Asynchronous Federated Learning FrameworkCode0
Improving Differentially Private SGD via Randomly Sparsified GradientsCode0
A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with AutoencoderCode0
FedFetch: Faster Federated Learning with Adaptive Downstream PrefetchingCode0
Layer-wise Linear Mode ConnectivityCode0
Use of Air Quality Sensor Network Data for Real-time Pollution-Aware POI SuggestionCode0
BROADCAST: Reducing Both Stochastic and Compression Noise to Robustify Communication-Efficient Federated LearningCode0
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic SurgeryCode0
FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID DataCode0
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing TasksCode0
Differentially private partitioned variational inferenceCode0
Whole-brain radiomics for clustered federated personalization in brain tumor segmentationCode0
FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures OptimizerCode0
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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