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 701750 of 6771 papers

TitleStatusHype
Exploiting Label Skews in Federated Learning with Model ConcatenationCode1
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated LearningCode1
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT ImagingCode1
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion AttacksCode1
Addressing Algorithmic Disparity and Performance Inconsistency in Federated LearningCode1
CaPC Learning: Confidential and Private Collaborative LearningCode1
Model-Contrastive Federated LearningCode1
Modeling Global Distribution for Federated Learning with Label Distribution SkewCode1
Agnostic Federated LearningCode1
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable DevicesCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State MatchingCode1
Fair Federated Learning under Domain Skew with Local Consistency and Domain DiversityCode1
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew ResilienceCode1
Multi-Level Branched Regularization for Federated LearningCode1
Fast-Convergent Federated Learning via Cyclic AggregationCode1
Fast Federated Learning by Balancing Communication Trade-OffsCode1
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated LearningCode1
Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object DetectionCode1
Fault-Tolerant Federated Reinforcement Learning with Theoretical GuaranteeCode1
Node Selection Toward Faster Convergence for Federated Learning on Non-IID DataCode1
Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI ModalitiesCode1
Feature Inference Attack on Model Predictions in Vertical Federated LearningCode1
Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and PrivacyCode1
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and CorrectionCode1
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation NoiseCode1
FedAdapt: Adaptive Offloading for IoT Devices in Federated LearningCode1
A Hybrid Self-Supervised Learning Framework for Vertical Federated LearningCode1
FedALA: Adaptive Local Aggregation for Personalized Federated LearningCode1
One-Shot Federated Conformal PredictionCode1
One-shot Federated Learning via Synthetic Distiller-Distillate CommunicationCode1
Bayesian Framework for Gradient LeakageCode1
Decentralized Federated Learning: A Segmented Gossip ApproachCode1
FedBABU: Towards Enhanced Representation for Federated Image ClassificationCode1
FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic SystemsCode1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
FedCMR: Federated Cross-Modal RetrievalCode1
Benchmarking Differential Privacy and Federated Learning for BERT ModelsCode1
FedBN: Federated Learning on Non-IID Features via Local Batch NormalizationCode1
FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based OptimizationCode1
FedCD: Improving Performance in non-IID Federated LearningCode1
Optimal Accounting of Differential Privacy via Characteristic FunctionCode1
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
Federated Transfer Learning for EEG Signal ClassificationCode1
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated LearningCode1
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural NetworksCode1
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous DataCode1
Show:102550
← PrevPage 15 of 136Next →

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