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 351400 of 6771 papers

TitleStatusHype
EHRFL: Federated Learning Framework for Institution-Specific Model Construction using Electronic Health RecordsCode1
DENSE: Data-Free One-Shot Federated LearningCode1
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and QuantizationCode1
Agnostic Federated LearningCode1
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
Federated Domain Generalization for Image Recognition via Cross-Client Style TransferCode1
Collaborative Fairness in Federated LearningCode1
Federated Ensemble-Directed Offline Reinforcement LearningCode1
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 KilobytesCode1
Combating Exacerbated Heterogeneity for Robust Models in Federated LearningCode1
Communication-Efficient Adaptive Federated LearningCode1
FedGS: Federated Graph-based Sampling with Arbitrary Client AvailabilityCode1
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
A Hybrid Self-Supervised Learning Framework for Vertical Federated LearningCode1
Federated Knowledge DistillationCode1
Accumulative Poisoning Attacks on Real-time DataCode1
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance ReductionCode1
Adaptive Federated OptimizationCode1
Communication-Efficient Federated Learning with Accelerated Client GradientCode1
Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image ClassificationCode1
Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side DistillationCode1
Communication-Efficient Federated Learning with Compensated Overlap-FedAvgCode1
Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoTCode1
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball MomentumCode1
Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud ComputingCode1
Communication-Efficient Learning of Deep Networks from Decentralized DataCode1
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data DetectionCode1
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITSCode1
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing ClientsCode1
Applied Federated Learning: Improving Google Keyboard Query SuggestionsCode1
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and MethodCode1
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class ImbalanceCode1
Flame: Simplifying Topology Extension in Federated LearningCode1
Continual Local Training for Better Initialization of Federated ModelsCode1
Federated Learning under Distributed Concept DriftCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
Specialized federated learning using a mixture of expertsCode1
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime DetectionCode1
Proportional Fairness in Federated LearningCode1
ALI-DPFL: Differentially Private Federated Learning with Adaptive Local IterationsCode1
CRFL: Certifiably Robust Federated Learning against Backdoor AttacksCode1
A Better Alternative to Error Feedback for Communication-Efficient Distributed LearningCode1
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative LearningCode1
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID DataCode1
DAGER: Exact Gradient Inversion for Large Language ModelsCode1
Exploiting Label Skews in Federated Learning with Model ConcatenationCode1
Federated Learning with Diffusion Models for Privacy-Sensitive Vision TasksCode1
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge DevicesCode1
A Dynamic Weighted Federated Learning for Android Malware ClassificationCode1
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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