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 751800 of 6771 papers

TitleStatusHype
Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoTCode1
Federated Mutual LearningCode1
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint LearningCode1
Free-rider Attacks on Model Aggregation in Federated LearningCode1
Federated Learning Meets Multi-objective OptimizationCode1
A Better Alternative to Error Feedback for Communication-Efficient Distributed LearningCode1
FedCD: Improving Performance in non-IID Federated LearningCode1
Personalized Federated Learning with Moreau EnvelopesCode1
Byzantine-Robust Learning on Heterogeneous Datasets via BucketingCode1
The OARF Benchmark Suite: Characterization and Implications for Federated Learning SystemsCode1
Locally Private Graph Neural NetworksCode1
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret SharingCode1
An Efficient Framework for Clustered Federated LearningCode1
UVeQFed: Universal Vector Quantization for Federated LearningCode1
A Distributed Trust Framework for Privacy-Preserving Machine LearningCode1
Continual Local Training for Better Initialization of Federated ModelsCode1
FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID DataCode1
Pretraining Federated Text Models for Next Word PredictionCode1
Multi-Center Federated Learning: Clients Clustering for Better PersonalizationCode1
DBA: Distributed Backdoor Attacks against Federated LearningCode1
Omnidirectional Transfer for Quasilinear Lifelong LearningCode1
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated LearningCode1
Federated Transfer Learning for EEG Signal ClassificationCode1
SplitFed: When Federated Learning Meets Split LearningCode1
Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture SearchCode1
Lightwave Power Transfer for Federated Learning-based Wireless NetworksCode1
FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated LearningCode1
A Blockchain-based Decentralized Federated Learning Framework with Committee ConsensusCode1
Inverting Gradients -- How easy is it to break privacy in federated learning?Code1
End-to-End Evaluation of Federated Learning and Split Learning for Internet of ThingsCode1
Privacy-Preserving News Recommendation Model LearningCode1
Privacy-preserving Traffic Flow Prediction: A Federated Learning ApproachCode1
Ternary Compression for Communication-Efficient Federated LearningCode1
Federated Continual Learning with Weighted Inter-client TransferCode1
Evaluation Framework For Large-scale Federated LearningCode1
Adaptive Federated OptimizationCode1
FedCoin: A Peer-to-Peer Payment System for Federated LearningCode1
Device Heterogeneity in Federated Learning: A Superquantile ApproachCode1
PrivacyFL: A simulator for privacy-preserving and secure federated learningCode1
Federated Learning with Matched AveragingCode1
Salvaging Federated Learning by Local AdaptationCode1
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE ResultsCode1
iDLG: Improved Deep Leakage from GradientsCode1
FedDANE: A Federated Newton-Type MethodCode1
Think Locally, Act Globally: Federated Learning with Local and Global RepresentationsCode1
Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT NetworksCode1
Generative Models for Effective ML on Private, Decentralized DatasetsCode1
Practical Federated Gradient Boosting Decision TreesCode1
Federated Learning over Wireless Networks: Convergence Analysis and Resource AllocationCode1
Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge IntelligenceCode1
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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