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 16511700 of 6771 papers

TitleStatusHype
FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?Code0
F3: Fair and Federated Face Attribute Classification with Heterogeneous DataCode0
FedCCRL: Federated Domain Generalization with Cross-Client Representation LearningCode0
Exploring Data Redundancy in Real-world Image Classification through Data SelectionCode0
Exploiting Unintended Feature Leakage in Collaborative LearningCode0
Exploring Selective Layer Fine-Tuning in Federated LearningCode0
Cross-Silo Heterogeneous Model Federated Multitask LearningCode0
Explicit Personalization and Local Training: Double Communication Acceleration in Federated LearningCode0
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat DetectionCode0
Personalized Federated Learning with Multiple Known ClustersCode0
Explainable Federated Bayesian Causal Inference and Its Application in Advanced ManufacturingCode0
Personalized Multi-tier Federated LearningCode0
Exact Penalty Method for Federated LearningCode0
Experimenting with Emerging RISC-V Systems for Decentralised Machine LearningCode0
Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID DataCode0
Evaluating Federated Kolmogorov-Arnold Networks on Non-IID DataCode0
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
Experimenting with Normalization Layers in Federated Learning on non-IID scenariosCode0
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous DataCode0
Ensemble Distillation for Robust Model Fusion in Federated LearningCode0
Equipping Federated Graph Neural Networks with Structure-aware Group FairnessCode0
Energy Prediction using Federated LearningCode0
Enhanced Security and Privacy via Fragmented Federated LearningCode0
Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep LearningCode0
Energy-Efficient Federated Learning for AIoT using Clustering MethodsCode0
PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated LearningCode0
Encrypted machine learning of molecular quantum propertiesCode0
Clustered Federated Learning via Embedding DistributionsCode0
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy ConstraintsCode0
Clustered Federated Learning based on Nonconvex Pairwise FusionCode0
Enable the Right to be Forgotten with Federated Client Unlearning in Medical ImagingCode0
Distributed Sign Momentum with Local Steps for Training TransformersCode0
Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEsCode0
Embedding Byzantine Fault Tolerance into Federated Learning via Virtual Data-Driven Consistency Scoring PluginCode0
Privacy Amplification by DecentralizationCode0
Privacy Amplification for Federated Learning via User Sampling and Wireless AggregationCode0
Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples via Least-Squares SolutionCode0
Empowering Data Mesh with Federated LearningCode0
End-to-End Verifiable Decentralized Federated LearningCode0
Estimation of Microphone Clusters in Acoustic Sensor Networks using Unsupervised Federated LearningCode0
Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning ApproachCode0
Privacy-Preserving Classification with Secret Vector MachinesCode0
Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version)Code0
Privacy-Preserving Distributed Learning for Residential Short-Term Load ForecastingCode0
FAA-CLIP: Federated Adversarial Adaptation of CLIPCode0
A Potential Game Perspective in Federated LearningCode0
Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based modelCode0
Privacy Risks Analysis and Mitigation in Federated Learning for Medical ImagesCode0
CrowdGuard: Federated Backdoor Detection in Federated LearningCode0
Efficient and Robust Regularized Federated RecommendationCode0
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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