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 201250 of 6771 papers

TitleStatusHype
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
Language-Guided Transformer for Federated Multi-Label ClassificationCode1
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 KilobytesCode1
Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide ImagesCode1
Exploiting Label Skews in Federated Learning with Model ConcatenationCode1
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain ShiftsCode1
Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative LatentsCode1
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball MomentumCode1
Federated Learning with Diffusion Models for Privacy-Sensitive Vision TasksCode1
FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed SettingsCode1
VeryFL: A Verify Federated Learning Framework Embedded with BlockchainCode1
A Survey on Vulnerability of Federated Learning: A Learning Algorithm PerspectiveCode1
FedHCA^2: Towards Hetero-Client Federated Multi-Task LearningCode1
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous ClientsCode1
FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality FusionCode1
FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement LearningCode1
Federated Learning for Generalization, Robustness, Fairness: A Survey and BenchmarkCode1
Data Valuation and Detections in Federated LearningCode1
Exploring Federated Unlearning: Review, Comparison, and InsightsCode1
Adaptive Test-Time Personalization for Federated LearningCode1
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated LearningCode1
Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object DetectionCode1
Serverless Federated Learning with flwr-serverlessCode1
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive OptimizationCode1
FLTracer: Accurate Poisoning Attack Provenance in Federated LearningCode1
Passive Inference Attacks on Split Learning via Adversarial RegularizationCode1
VFLAIR: A Research Library and Benchmark for Vertical Federated LearningCode1
Sample-Driven Federated Learning for Energy-Efficient and Real-Time IoT SensingCode1
FedFed: Feature Distillation against Data Heterogeneity in Federated LearningCode1
FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated LearningCode1
On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and BeyondCode1
FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of ThingsCode1
Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual PerspectiveCode1
Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity LearningCode1
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical DatasetsCode1
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware SchedulerCode1
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous DrivingCode1
Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace TrainingCode1
Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace TrainingCode1
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
Towards Energy-Aware Federated Traffic Prediction for Cellular NetworksCode1
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation LearningCode1
FedJudge: Federated Legal Large Language ModelCode1
FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental RegularizationCode1
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated LearningCode1
Bias Propagation in Federated LearningCode1
Post-Deployment Adaptation with Access to Source Data via Federated Learning and Source-Target Remote Gradient AlignmentCode1
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot InteractionCode1
FwdLLM: Efficient FedLLM using Forward GradientCode1
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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