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 56015650 of 6771 papers

TitleStatusHype
Personalized Federated Learning over non-IID Data for Indoor Localization0
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platformCode1
Federated Learning as a Mean-Field Game0
Federated Model Search via Reinforcement Learning0
Federated Learning with Downlink Device Selection0
Federated Learning for Multi-Center Imaging Diagnostics: A Study in Cardiovascular DiseaseCode1
DER Forecast using Privacy Preserving Federated Learning0
Management of Resource at the Network Edge for Federated Learning0
RoFL: Robustness of Secure Federated LearningCode1
Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networks0
SplitAVG: A heterogeneity-aware federated deep learning method for medical imagingCode1
Differentially private federated deep learning for multi-site medical image segmentationCode0
Memory-aware curriculum federated learning for breast cancer classificationCode0
Optimizing the Numbers of Queries and Replies in Federated Learning with Differential PrivacyCode0
FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systems0
Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning0
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates0
Segmented Federated Learning for Adaptive Intrusion Detection System0
Gradient-Leakage Resilient Federated LearningCode1
On Bridging Generic and Personalized Federated Learning for Image ClassificationCode1
FedCMR: Federated Cross-Modal RetrievalCode1
FedMix: Approximation of Mixup under Mean Augmented Federated LearningCode1
Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data0
Faithful Edge Federated Learning: Scalability and Privacy0
UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach0
Personalized Federated Learning with Gaussian ProcessesCode1
A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points0
Weight Divergence Driven Divide-and-Conquer Approach for Optimal Federated Learning from non-IID Data0
Federated Dynamic Spectrum Access0
A Comprehensive Survey of Incentive Mechanism for Federated Learning0
Over-the-Air Federated Multi-Task Learning0
Reward-Based 1-bit Compressed Federated Distillation on Blockchain0
Benchmarking Differential Privacy and Federated Learning for BERT ModelsCode1
Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approach0
Implicit Gradient Alignment in Distributed and Federated Learning0
Federated Graph Classification over Non-IID GraphsCode1
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy0
Subgraph Federated Learning with Missing Neighbor GenerationCode1
Privacy Threats Analysis to Secure Federated Learning0
Personalized Federated Learning with Contextualized Generalization0
Federated Noisy Client LearningCode0
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning0
A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective OptimizationCode0
Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective0
FLRA: A Reference Architecture for Federated Learning Systems0
A Vertical Federated Learning Framework for Graph Convolutional Network0
An Efficient Approach for Cross-Silo Federated Learning to RankCode1
FedCM: Federated Learning with Client-level MomentumCode1
Affect-driven Ordinal Engagement Measurement from Video0
Federated Learning with Positive and Unlabeled DataCode1
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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