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 501550 of 6771 papers

TitleStatusHype
Personalized Federated Learning With GraphCode1
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous DrivingCode1
Sky Computing: Accelerating Geo-distributed Computing in Federated LearningCode1
Privacy Leakage of Adversarial Training Models in Federated Learning SystemsCode1
LAMP: Extracting Text from Gradients with Language Model PriorsCode1
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTCode1
OLIVE: Oblivious Federated Learning on Trusted Execution Environment against the risk of sparsificationCode1
UA-FedRec: Untargeted Attack on Federated News RecommendationCode1
Federated Learning of Generative Image Priors for MRI ReconstructionCode1
APPFL: Open-Source Software Framework for Privacy-Preserving Federated LearningCode1
FL_PyTorch: optimization research simulator for federated learningCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated LearningCode1
Byzantine-Robust Decentralized Learning via ClippedGossipCode1
Proportional Fairness in Federated LearningCode1
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?Code1
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional NetworksCode1
Speeding up Heterogeneous Federated Learning with Sequentially Trained SuperclientsCode1
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated LearningCode1
FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image SynthesisCode1
Communication-Efficient Federated Learning with Accelerated Client GradientCode1
FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous ClientsCode1
Robust Federated Learning With Noisy and Heterogeneous ClientsCode1
Learn From Others and Be Yourself in Heterogeneous Federated LearningCode1
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning SettingsCode1
FedFR: Joint Optimization Federated Framework for Generic and Personalized Face RecognitionCode1
DENSE: Data-Free One-Shot Federated LearningCode1
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical ImagesCode1
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning BetterCode1
Federated Learning with Superquantile Aggregation for Heterogeneous DataCode1
Analysis and Evaluation of Synchronous and Asynchronous FLchainCode1
Scatterbrained: A flexible and expandable pattern for decentralized machine learningCode1
Specificity-Preserving Federated Learning for MR Image ReconstructionCode1
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless NetworksCode1
Catastrophic Data Leakage in Vertical Federated LearningCode1
Personalized Federated Learning with Adaptive Batchnorm for HealthcareCode1
Projected Federated Averaging with Heterogeneous Differential PrivacyCode1
Evaluating Gradient Inversion Attacks and Defenses in Federated LearningCode1
SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated LearningCode1
Local Learning Matters: Rethinking Data Heterogeneity in Federated LearningCode1
FedCV: A Federated Learning Framework for Diverse Computer Vision TasksCode1
Decentralized Federated Learning through Proxy Model SharingCode1
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial IntelligenceCode1
Differentially Private Federated Learning on Heterogeneous DataCode1
Personalized Federated Learning through Local MemorizationCode1
Power Allocation for Wireless Federated Learning using Graph Neural NetworksCode1
Eluding Secure Aggregation in Federated Learning via Model InconsistencyCode1
Federated Learning Based on Dynamic RegularizationCode1
Bayesian Framework for Gradient LeakageCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
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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