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 66016650 of 6771 papers

TitleStatusHype
Hyper-Sphere Quantization: Communication-Efficient SGD for Federated LearningCode0
Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for Federated Continual LearningCode0
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy JudgmentCode0
AutoDFL: A Scalable and Automated Reputation-Aware Decentralized Federated LearningCode0
Fed-ensemble: Improving Generalization through Model Ensembling in Federated LearningCode0
FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated LearningCode0
IFedAvg: Interpretable Data-Interoperability for Federated LearningCode0
When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User InteractionsCode0
Privacy-Preserved Automated Scoring using Federated Learning for Educational ResearchCode0
Achieving Distributive Justice in Federated Learning via Uncertainty QuantificationCode0
Impact of Network Topology on Byzantine Resilience in Decentralized Federated LearningCode0
Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for FCLCode0
Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning ApproachCode0
FedDWA: Personalized Federated Learning with Dynamic Weight AdjustmentCode0
Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated ModelsCode0
Venn: Resource Management for Collaborative Learning JobsCode0
One-shot Federated Learning Methods: A Practical GuideCode0
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical RepresentationsCode0
Federated Learning with Convex Global and Local ConstraintsCode0
A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated LearningCode0
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated LearningCode0
Reward Systems for Trustworthy Medical Federated LearningCode0
Tackling Selfish Clients in Federated LearningCode0
One-shot Federated Learning without Server-side TrainingCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
Improving Communication Efficiency of Federated Distillation via Accumulating Local UpdatesCode0
RFID based Health Adherence Medicine Case Using Fair Federated LearningCode0
Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning MethodsCode0
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy LabelsCode0
Improving Federated Aggregation with Deep Unfolding NetworksCode0
One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model DiversityCode0
RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure AggregationCode0
Improving Federated Learning Personalization via Model Agnostic Meta LearningCode0
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive SpaceCode0
FedDebug: Systematic Debugging for Federated Learning ApplicationsCode0
Venomancer: Towards Imperceptible and Target-on-Demand Backdoor Attacks in Federated LearningCode0
Privacy-Preserving Classification with Secret Vector MachinesCode0
Analysis of Privacy Leakage in Federated Large Language ModelsCode0
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single LayersCode0
Privacy-preserving Continual Federated Clustering via Adaptive Resonance TheoryCode0
Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited StalenessCode0
Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image AugmentationCode0
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential PrivacyCode0
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMCode0
Improving Response Time of Home IoT Services in Federated LearningCode0
Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version)Code0
RiM: Record, Improve and Maintain Physical Well-being using Federated LearningCode0
When Do Curricula Work in Federated Learning?Code0
FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue ClassificationCode0
UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous DataCode0
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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