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 501550 of 6771 papers

TitleStatusHype
Federated Bayesian Optimization via Thompson SamplingCode1
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural NetworksCode1
Federated Continual Learning with Weighted Inter-client TransferCode1
FedCM: Federated Learning with Client-level MomentumCode1
Federated Foundation Models on Heterogeneous Time SeriesCode1
FedCMR: Federated Cross-Modal RetrievalCode1
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial IntelligenceCode1
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware SchedulerCode1
FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated LearningCode1
FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated ClientsCode1
FedBE: Making Bayesian Model Ensemble Applicable to Federated LearningCode1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language ProcessingCode1
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous DrivingCode1
Asynchronous Federated Continual LearningCode1
FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental RegularizationCode1
A Survey on Vulnerability of Federated Learning: A Learning Algorithm PerspectiveCode1
FedDefender: Client-Side Attack-Tolerant Federated LearningCode1
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency SpaceCode1
FedDisco: Federated Learning with Discrepancy-Aware CollaborationCode1
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous DrivingCode1
Exploring Federated Unlearning: Review, Comparison, and InsightsCode1
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT NetworksCode1
A Survey for Federated Learning Evaluations: Goals and MeasuresCode1
Asynchronous Federated Learning for Edge-assisted Vehicular NetworksCode1
APPFL: Open-Source Software Framework for Privacy-Preserving Federated LearningCode1
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a ServiceCode1
Attack of the Tails: Yes, You Really Can Backdoor Federated LearningCode1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated LearningCode1
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning SettingsCode1
Federated Adaptation for Foundation Model-based RecommendationsCode1
Federated Class-Incremental LearningCode1
Auditing Privacy Defenses in Federated Learning via Generative Gradient LeakageCode1
Applied Federated Learning: Improving Google Keyboard Query SuggestionsCode1
Federated Class-Incremental Learning with New-Class Augmented Self-DistillationCode1
FedAdapter: Efficient Federated Learning for Modern NLPCode1
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation LearningCode1
Federated Deep Learning Meets Autonomous Vehicle Perception: Design and VerificationCode1
DENSE: Data-Free One-Shot Federated LearningCode1
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental DesignCode1
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
A Dynamic Weighted Federated Learning for Android Malware ClassificationCode1
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning BetterCode1
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated LearningCode1
ARFED: Attack-Resistant Federated averaging based on outlier eliminationCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient DescentCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime DetectionCode1
Benchmarking Differential Privacy and Federated Learning for BERT ModelsCode1
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and CorrectionCode1
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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