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 251300 of 6771 papers

TitleStatusHype
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding AggregationCode1
Efficient On-device Training via Gradient FilteringCode1
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial IntelligenceCode1
Federated Learning Enables Big Data for Rare Cancer Boundary DetectionCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
Edge Federated Learning Via Unit-Modulus Over-The-Air ComputationCode1
Dynamic Defense Against Byzantine Poisoning Attacks in Federated LearningCode1
FedGiA: An Efficient Hybrid Algorithm for Federated LearningCode1
Auditing Privacy Defenses in Federated Learning via Generative Gradient LeakageCode1
Exploring Federated Unlearning: Review, Comparison, and InsightsCode1
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class ImbalanceCode1
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data SubspacesCode1
EHRFL: Federated Learning Framework for Institution-Specific Model Construction using Electronic Health RecordsCode1
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated LearningCode1
Dopamine: Differentially Private Federated Learning on Medical DataCode1
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a ServiceCode1
BAFFLE: A Baseline of Backpropagation-Free Federated LearningCode1
Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked VehiclesCode1
Distantly Supervised Relation Extraction in Federated SettingsCode1
Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient DescentCode1
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
Differentially Private Learning with Adaptive ClippingCode1
Differentially Private Federated Learning on Heterogeneous DataCode1
Differentially Private Vertical Federated ClusteringCode1
A New Federated Learning Framework Against Gradient Inversion AttacksCode1
An Empirical Study of Personalized Federated LearningCode1
Differentially Private Federated Learning: A Client Level PerspectiveCode1
An Efficient Framework for Clustered Federated LearningCode1
DENSE: Data-Free One-Shot Federated LearningCode1
Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service DetectionCode1
Detecting Backdoor Attacks in Federated Learning via Direction Alignment InspectionCode1
DistFL: Distribution-aware Federated Learning for Mobile ScenariosCode1
Device Heterogeneity in Federated Learning: A Superquantile ApproachCode1
APPFL: Open-Source Software Framework for Privacy-Preserving Federated LearningCode1
A Federated Data-Driven Evolutionary AlgorithmCode1
Applied Federated Learning: Improving Google Keyboard Query SuggestionsCode1
ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRICode1
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime DetectionCode1
A federated graph neural network framework for privacy-preserving personalizationCode1
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and ComparisonCode1
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret SharingCode1
A Survey for Federated Learning Evaluations: Goals and MeasuresCode1
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth LandscapeCode1
EasyFL: A Low-code Federated Learning Platform For DummiesCode1
Active Membership Inference Attack under Local Differential Privacy in Federated LearningCode1
Asynchronous Federated Continual LearningCode1
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public TransportationCode1
An Efficient Approach for Cross-Silo Federated Learning to RankCode1
A Survey on Vulnerability of Federated Learning: A Learning Algorithm PerspectiveCode1
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental DesignCode1
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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