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 851900 of 6771 papers

TitleStatusHype
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural NetworksCode0
FedOS: using open-set learning to stabilize training in federated learningCode0
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing TasksCode0
FedCVT: Semi-supervised Vertical Federated Learning with Cross-view TrainingCode0
FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature DriftCode0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
FedSecurity: Benchmarking Attacks and Defenses in Federated Learning and Federated LLMsCode0
A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraintsCode0
An Improved Algorithm for Clustered Federated LearningCode0
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated LearningCode0
FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge DevicesCode0
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical ImagingCode0
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP OptimizationCode0
A New Perspective to Boost Performance Fairness for Medical Federated LearningCode0
A New Perspective on Privacy Protection in Federated Learning with Granular-Ball ComputingCode0
FLStore: Efficient Federated Learning Storage for non-training workloadsCode0
FedLWS: Federated Learning with Adaptive Layer-wise Weight ShrinkingCode0
A Differentially Private Blockchain-Based Approach for Vertical Federated LearningCode0
FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge InjectionCode0
A Client-server Deep Federated Learning for Cross-domain Surgical Image SegmentationCode0
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed DataCode0
A deep cut into Split Federated Self-supervised LearningCode0
An Equivalence Between Data Poisoning and Byzantine Gradient AttacksCode0
FedLion: Faster Adaptive Federated Optimization with Fewer CommunicationCode0
FedLPA: One-shot Federated Learning with Layer-Wise Posterior AggregationCode0
Adaptive Guidance for Local Training in Heterogeneous Federated LearningCode0
FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed LearningCode0
FedKD-hybrid: Federated Hybrid Knowledge Distillation for Lithography Hotspot DetectionCode0
FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation ModelsCode0
FedICT: Federated Multi-task Distillation for Multi-access Edge ComputingCode0
Breaching FedMD: Image Recovery via Paired-Logits Inversion AttackCode0
FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile DevicesCode0
Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA AllocationCode0
FedPH: Privacy-enhanced Heterogeneous Federated LearningCode0
FedHe: Heterogeneous Models and Communication-Efficient Federated LearningCode0
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation CompletenessCode0
FedIMPUTE: Privacy-Preserving Missing Value Imputation for Multi-Site Heterogeneous Electronic Health RecordsCode0
FedMultimodal: A Benchmark For Multimodal Federated LearningCode0
Brain Age Estimation Using Structural MRI: A Clustered Federated Learning ApproachCode0
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss ApproximationsCode0
Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated LearningCode0
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients InspectionCode0
FedFT: Improving Communication Performance for Federated Learning with Frequency Space TransformationCode0
An Element-Wise Weights Aggregation Method for Federated LearningCode0
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
FedFetch: Faster Federated Learning with Adaptive Downstream PrefetchingCode0
Achieving Model Fairness in Vertical Federated LearningCode0
BOBA: Byzantine-Robust Federated Learning with Label SkewnessCode0
Addressing Heterogeneity in Federated Learning via Distributional TransformationCode0
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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