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 701750 of 6771 papers

TitleStatusHype
Provable Defense against Privacy Leakage in Federated Learning from Representation PerspectiveCode1
Unleashing the Tiger: Inference Attacks on Split LearningCode1
Robust Federated Learning with Noisy LabelsCode1
Inverting Gradients - How easy is it to break privacy in federated learning?Code1
Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge NetworksCode1
Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design ApproachCode1
A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated LearningCode1
Federated Composite OptimizationCode1
Federated Knowledge DistillationCode1
Optimal Client Sampling for Federated LearningCode1
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech RecognitionCode1
Throughput-Optimal Topology Design for Cross-Silo Federated LearningCode1
Feature Inference Attack on Model Predictions in Vertical Federated LearningCode1
Federated Bayesian Optimization via Thompson SamplingCode1
R-GAP: Recursive Gradient Attack on PrivacyCode1
FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven MeasureCode1
TextHide: Tackling Data Privacy in Language Understanding TasksCode1
Oort: Efficient Federated Learning via Guided Participant SelectionCode1
Federated Learning via Posterior Averaging: A New Perspective and Practical AlgorithmsCode1
Specialized federated learning using a mixture of expertsCode1
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous ClientsCode1
Model-sharing Games: Analyzing Federated Learning Under Voluntary ParticipationCode1
Practical One-Shot Federated Learning for Cross-Silo SettingCode1
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANsCode1
Over-the-Air Federated Learning from Heterogeneous DataCode1
Federated Learning for Computational Pathology on Gigapixel Whole Slide ImagesCode1
FLAME: Differentially Private Federated Learning in the Shuffle ModelCode1
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient DescentCode1
FedBE: Making Bayesian Model Ensemble Applicable to Federated LearningCode1
Collaborative Fairness in Federated LearningCode1
Performance Optimization for Federated Person Re-identification via Benchmark AnalysisCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
WAFFLE: Watermarking in Federated LearningCode1
Addressing Class Imbalance in Federated LearningCode1
Distantly Supervised Relation Extraction in Federated SettingsCode1
Mime: Mimicking Centralized Stochastic Algorithms in Federated LearningCode1
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID DatasetsCode1
Dynamic Defense Against Byzantine Poisoning Attacks in Federated LearningCode1
Flower: A Friendly Federated Learning Research FrameworkCode1
Group Knowledge Transfer: Federated Learning of Large CNNs at the EdgeCode1
IBM Federated Learning: an Enterprise Framework White Paper V0.1Code1
Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog NetworksCode1
Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge ComputingCode1
Learn distributed GAN with Temporary DiscriminatorsCode1
Data Poisoning Attacks Against Federated Learning SystemsCode1
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT ImagingCode1
Differentially private cross-silo federated learningCode1
Attack of the Tails: Yes, You Really Can Backdoor Federated LearningCode1
Defending against Backdoors in Federated Learning with Robust Learning RateCode1
Multi-Armed Bandit Based Client Scheduling for Federated LearningCode1
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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