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 901950 of 6771 papers

TitleStatusHype
FedKD-hybrid: Federated Hybrid Knowledge Distillation for Lithography Hotspot DetectionCode0
FedLion: Faster Adaptive Federated Optimization with Fewer CommunicationCode0
FedNS: A Fast Sketching Newton-Type Algorithm for Federated LearningCode0
Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA AllocationCode0
Brain Age Estimation Using Structural MRI: A Clustered Federated Learning ApproachCode0
FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile DevicesCode0
Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated LearningCode0
FedHe: Heterogeneous Models and Communication-Efficient Federated LearningCode0
FedPH: Privacy-enhanced Heterogeneous Federated LearningCode0
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP OptimizationCode0
FedGS: Federated Gradient Scaling for Heterogeneous Medical Image SegmentationCode0
An Element-Wise Weights Aggregation Method for Federated LearningCode0
FedHarmony: Unlearning Scanner Bias with Distributed DataCode0
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation CompletenessCode0
Achieving Model Fairness in Vertical Federated LearningCode0
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss ApproximationsCode0
BOBA: Byzantine-Robust Federated Learning with Label SkewnessCode0
FedFT: Improving Communication Performance for Federated Learning with Frequency Space TransformationCode0
Addressing Heterogeneity in Federated Learning via Distributional TransformationCode0
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET DenoisingCode0
A deep cut into Split Federated Self-supervised LearningCode0
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated LearningCode0
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients InspectionCode0
FedMultimodal: A Benchmark For Multimodal Federated LearningCode0
Addressing Data Quality Decompensation in Federated Learning via Dynamic Client SelectionCode0
FedExP: Speeding Up Federated Averaging via ExtrapolationCode0
Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous ClientsCode0
FedFetch: Faster Federated Learning with Adaptive Downstream PrefetchingCode0
An effective and efficient green federated learning method for one-layer neural networksCode0
Federated User Preference Modeling for Privacy-Preserving Cross-Domain RecommendationCode0
Federated Unlearning via Class-Discriminative PruningCode0
Blockchain-empowered Federated Learning: Benefits, Challenges, and SolutionsCode0
Federated Visual Classification with Real-World Data DistributionCode0
A New Perspective on Privacy Protection in Federated Learning with Granular-Ball ComputingCode0
Federated Two Stage Decoupling With Adaptive Personalization LayersCode0
A New Perspective to Boost Performance Fairness for Medical Federated LearningCode0
Federated Survival ForestsCode0
Federated Unlearning Made Practical: Seamless Integration via Negated Pseudo-GradientsCode0
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate GradientsCode0
FedFOR: Stateless Heterogeneous Federated Learning with First-Order RegularizationCode0
FedQV: Leveraging Quadratic Voting in Federated LearningCode0
FedICT: Federated Multi-task Distillation for Multi-access Edge ComputingCode0
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
FedRec: Federated Learning of Universal Receivers over Fading ChannelsCode0
FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant ClientsCode0
FedRIR: Rethinking Information Representation in Federated LearningCode0
Federated singular value decomposition for high dimensional dataCode0
Byzantine-Robust Variance-Reduced Federated Learning over Distributed Non-i.i.d. DataCode0
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited EdgeCode0
Federated Representation Learning in the Under-Parameterized RegimeCode0
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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