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 651700 of 6771 papers

TitleStatusHype
Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity LearningCode1
Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked VehiclesCode1
Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient DescentCode1
Generative Models for Effective ML on Private, Decentralized DatasetsCode1
Federated Learning with Partial Model PersonalizationCode1
BAFFLE: A Baseline of Backpropagation-Free Federated LearningCode1
Federated Learning with Spiking Neural NetworksCode1
Dopamine: Differentially Private Federated Learning on Medical DataCode1
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
Gradient-Leakage Resilient Federated LearningCode1
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated LearningCode1
GPFedRec: Graph-guided Personalization for Federated RecommendationCode1
DYNAFED: Tackling Client Data Heterogeneity with Global DynamicsCode1
Dual-Personalizing Adapter for Federated Foundation ModelsCode1
Group Knowledge Transfer: Federated Learning of Large CNNs at the EdgeCode1
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical ImagesCode1
Dynamic Defense Against Byzantine Poisoning Attacks in Federated LearningCode1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language ProcessingCode1
Omnidirectional Transfer for Quasilinear Lifelong LearningCode1
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth LandscapeCode1
Byzantine-Robust Decentralized Learning via ClippedGossipCode1
Efficient passive membership inference attack in federated learningCode1
FedGiA: An Efficient Hybrid Algorithm for Federated LearningCode1
Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated LearningCode1
Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles Between Client Data SubspacesCode1
Efficient On-device Training via Gradient FilteringCode1
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding AggregationCode1
Differentially Private Federated Learning: A Client Level PerspectiveCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data SourcesCode1
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
Attack of the Tails: Yes, You Really Can Backdoor Federated LearningCode1
EHRFL: Federated Learning Framework for Institution-Specific Model Construction using Electronic Health RecordsCode1
Decentralized Federated Learning: A Segmented Gossip ApproachCode1
Improving Transferability of Network Intrusion Detection in a Federated Learning SetupCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning SettingsCode1
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated LearningCode1
Inverting Gradients - How easy is it to break privacy in federated learning?Code1
End-to-End Speech Recognition from Federated Acoustic ModelsCode1
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-BoostingCode1
Energy-Latency Attacks via Sponge PoisoningCode1
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical ImagingCode1
Estimation of Continuous Blood Pressure from PPG via a Federated Learning ApproachCode1
Evaluating Gradient Inversion Attacks and Defenses in Federated LearningCode1
Auditing Privacy Defenses in Federated Learning via Generative Gradient LeakageCode1
Proportional Fairness in Federated LearningCode1
Learn distributed GAN with Temporary DiscriminatorsCode1
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation NoiseCode1
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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