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 651700 of 6771 papers

TitleStatusHype
GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated LearningCode1
FedProto: Federated Prototype Learning across Heterogeneous ClientsCode1
Federated Learning with Fair AveragingCode1
PPFL: Privacy-preserving Federated Learning with Trusted Execution EnvironmentsCode1
End-to-End Speech Recognition from Federated Acoustic ModelsCode1
Federated Learning for Malware Detection in IoT DevicesCode1
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural NetworksCode1
Practical Defences Against Model Inversion Attacks for Split Neural NetworksCode1
PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNCode1
Model-Contrastive Federated LearningCode1
Federated Learning with Taskonomy for Non-IID DataCode1
A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge LearningCode1
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data SourcesCode1
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency SpaceCode1
Personalized Federated Learning using HypernetworksCode1
FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite OptimizationCode1
Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked VehiclesCode1
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable DevicesCode1
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of ThingsCode1
Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated LearningCode1
PFA: Privacy-preserving Federated Adaptation for Effective Model PersonalizationCode1
Heterogeneity for the Win: One-Shot Federated ClusteringCode1
Scalable federated machine learning with FEDnCode1
Practical and Private (Deep) Learning without Sampling or ShufflingCode1
Estimation of Continuous Blood Pressure from PPG via a Federated Learning ApproachCode1
Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation SystemsCode1
Label Leakage and Protection in Two-party Split LearningCode1
A Federated Data-Driven Evolutionary AlgorithmCode1
FedBN: Federated Learning on Non-IID Features via Local Batch NormalizationCode1
Exploiting Shared Representations for Personalized Federated LearningCode1
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian RegularizationCode1
Robust Federated Learning with Attack-Adaptive AggregationCode1
CaPC Learning: Confidential and Private Collaborative LearningCode1
Training Federated GANs with Theoretical Guarantees: A Universal Aggregation ApproachCode1
FedMood: Federated Learning on Mobile Health Data for Mood DetectionCode1
Federated Reconstruction: Partially Local Federated LearningCode1
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated LearningCode1
Federated Learning on Non-IID Data Silos: An Experimental StudyCode1
Federated Multi-Armed BanditsCode1
Edge Federated Learning Via Unit-Modulus Over-The-Air ComputationCode1
Dopamine: Differentially Private Federated Learning on Medical DataCode1
Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired ConsensusCode1
Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial SystemsCode1
PFL-MoE: Personalized Federated Learning Based on Mixture of ExpertsCode1
FLTrust: Byzantine-robust Federated Learning via Trust BootstrappingCode1
Learning from History for Byzantine Robust OptimizationCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
Personalized Federated Learning with First Order Model OptimizationCode1
Communication-Efficient Federated Learning with Compensated Overlap-FedAvgCode1
Provable Defense against Privacy Leakage in Federated Learning from Representation PerspectiveCode1
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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