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 251300 of 6771 papers

TitleStatusHype
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated LearningCode1
A Survey for Federated Learning Evaluations: Goals and MeasuresCode1
Understanding Hessian Alignment for Domain GeneralizationCode1
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated LearningCode1
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local IterationsCode1
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack DetectionCode1
Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated LearningCode1
Understanding the Role of Layer Normalization in Label-Skewed Federated LearningCode1
Towards Attack-tolerant Federated Learning via Critical Parameter AnalysisCode1
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a ServiceCode1
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated LearningCode1
FedPop: Federated Population-based Hyperparameter TuningCode1
Towards Instance-adaptive Inference for Federated LearningCode1
GIFD: A Generative Gradient Inversion Method with Feature Domain OptimizationCode1
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID DataCode1
Physics-Driven Spectrum-Consistent Federated Learning for Palmprint VerificationCode1
You Can Backdoor Personalized Federated LearningCode1
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental DesignCode1
Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image ClassificationCode1
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical ImagingCode1
Mitigating Communications Threats in Decentralized Federated Learning through Moving Target DefenseCode1
Heterogeneous Federated Learning: State-of-the-art and Research ChallengesCode1
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model InterpolationCode1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language ProcessingCode1
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured LearningCode1
FedDefender: Client-Side Attack-Tolerant Federated LearningCode1
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation LearningCode1
Locally Adaptive Federated LearningCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning BenchmarksCode1
FedDefender: Backdoor Attack Defense in Federated LearningCode1
FeSViBS: Federated Split Learning of Vision Transformer with Block SamplingCode1
FedNoisy: Federated Noisy Label Learning BenchmarkCode1
Personalized Federated Learning with Feature Alignment and Classifier CollaborationCode1
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural NetworkCode1
Federated Few-shot LearningCode1
Fedstellar: A Platform for Decentralized Federated LearningCode1
Federated Learning-based Vehicle Trajectory Prediction against CyberattacksCode1
Optimizing the Collaboration Structure in Cross-Silo Federated LearningCode1
Fast Optimal Locally Private Mean Estimation via Random ProjectionsCode1
Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated LearningCode1
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT NetworksCode1
Forgettable Federated Linear Learning with Certified Data UnlearningCode1
GPT-FL: Generative Pre-trained Model-Assisted Federated LearningCode1
Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated LearningCode1
FedDisco: Federated Learning with Discrepancy-Aware CollaborationCode1
Federated Learning of Gboard Language Models with Differential PrivacyCode1
Federated Conformal Predictors for Distributed Uncertainty QuantificationCode1
FedZero: Leveraging Renewable Excess Energy in Federated LearningCode1
Federated Prompt Learning for Weather Foundation Models on DevicesCode1
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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