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 9511000 of 6771 papers

TitleStatusHype
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients InspectionCode0
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss ApproximationsCode0
CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled RegularizationCode0
FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness ReweightingCode0
FedGS: Federated Gradient Scaling for Heterogeneous Medical Image SegmentationCode0
Decoupled Subgraph Federated LearningCode0
Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA AllocationCode0
An effective and efficient green federated learning method for one-layer neural networksCode0
FedFT: Improving Communication Performance for Federated Learning with Frequency Space TransformationCode0
Blockchain-empowered Federated Learning: Benefits, Challenges, and SolutionsCode0
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET DenoisingCode0
FedFOR: Stateless Heterogeneous Federated Learning with First-Order RegularizationCode0
FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile DevicesCode0
FedLPA: One-shot Federated Learning with Layer-Wise Posterior AggregationCode0
Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous ClientsCode0
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate GradientsCode0
FedExP: Speeding Up Federated Averaging via ExtrapolationCode0
Federated User Preference Modeling for Privacy-Preserving Cross-Domain RecommendationCode0
An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language ModelsCode0
Federated Visual Classification with Real-World Data DistributionCode0
An Interpretable Client Decision Tree Aggregation process for Federated LearningCode0
Federated Unlearning via Class-Discriminative PruningCode0
Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-GradientsCode0
Financial Data Analysis with Robust Federated Logistic RegressionCode0
Federated Two Stage Decoupling With Adaptive Personalization LayersCode0
Federated Submodel Optimization for Hot and Cold Data FeaturesCode0
Federated Stain Normalization for Computational PathologyCode0
Federated Survival ForestsCode0
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited EdgeCode0
Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID GraphsCode0
Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things ApplicationCode0
Federated singular value decomposition for high dimensional dataCode0
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain RecommendationCode0
Federated Prediction-Powered Inference from Decentralized DataCode0
Anomaly Detection through Unsupervised Federated LearningCode0
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature AugmentationCode0
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningCode0
Analysis of Privacy Leakage in Federated Large Language ModelsCode0
Federated Optimization for Heterogeneous NetworksCode0
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive ModelsCode0
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine LearningCode0
Federated Noisy Client LearningCode0
Federated Over-Air Subspace Tracking from Incomplete and Corrupted DataCode0
Federated Representation Learning in the Under-Parameterized RegimeCode0
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With SupplementCode0
Federated Neural Radiance FieldsCode0
FLrce: Resource-Efficient Federated Learning with Early-Stopping StrategyCode0
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental StudyCode0
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational EfficiencyCode0
Federated Multi-Task LearningCode0
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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