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 601650 of 6771 papers

TitleStatusHype
FedALA: Adaptive Local Aggregation for Personalized Federated LearningCode1
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client VectorsCode1
Exploring Federated Unlearning: Review, Comparison, and InsightsCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare ApplicationsCode1
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated LearningCode1
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural NetworksCode1
Benchmarking Differential Privacy and Federated Learning for BERT ModelsCode1
FedCM: Federated Learning with Client-level MomentumCode1
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware SchedulerCode1
FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated ClientsCode1
FedCorr: Multi-Stage Federated Learning for Label Noise CorrectionCode1
Bias Propagation in Federated LearningCode1
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and CorrectionCode1
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation LearningCode1
FedDefender: Backdoor Attack Defense in Federated LearningCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental RegularizationCode1
FedDisco: Federated Learning with Discrepancy-Aware CollaborationCode1
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous DrivingCode1
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous DrivingCode1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language ProcessingCode1
Federated Bayesian Optimization via Thompson SamplingCode1
PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental LearningCode1
Federated Class-Incremental Learning with New-Class Augmented Self-DistillationCode1
Federated Continual Learning with Weighted Inter-client TransferCode1
Federated Cross Learning for Medical Image SegmentationCode1
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless NetworksCode1
FedMood: Federated Learning on Mobile Health Data for Mood DetectionCode1
A Survey on Vulnerability of Federated Learning: A Learning Algorithm PerspectiveCode1
Federated Few-shot LearningCode1
Federated Foundation Models on Heterogeneous Time SeriesCode1
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 KilobytesCode1
Federated Graph Classification over Non-IID GraphsCode1
Federated Incremental Semantic SegmentationCode1
Federated Learning as Variational Inference: A Scalable Expectation Propagation ApproachCode1
Federated Learning Based on Dynamic RegularizationCode1
Bayesian Framework for Gradient LeakageCode1
Decentralized Federated Learning: A Segmented Gossip ApproachCode1
Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular DiseaseCode1
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITSCode1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and MethodCode1
A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge LearningCode1
Asynchronous Federated Continual LearningCode1
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated FeaturesCode1
Asynchronous Federated Learning for Edge-assisted Vehicular NetworksCode1
Federated Learning on Non-IID Graphs via Structural Knowledge SharingCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine 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