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 58015850 of 6771 papers

TitleStatusHype
RingFed: Reducing Communication Costs in Federated Learning on Non-IID Data0
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning0
Federated Action Recognition on Heterogeneous Embedded Devices0
RobustFed: A Truth Inference Approach for Robust Federated Learning0
Decentralized federated learning of deep neural networks on non-iid data0
An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging0
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning0
Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures0
Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach0
Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated LearningCode0
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo0
TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence0
IFedAvg: Interpretable Data-Interoperability for Federated LearningCode0
Federated Mixture of Experts0
Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced DataCode0
A Field Guide to Federated OptimizationCode0
Sparse Personalized Federated LearningCode0
Lithography Hotspot Detection via Heterogeneous Federated Learning with Local Adaptation0
Federated Learning with Dynamic Transformer for Text to Speech0
Personalized Federated Learning over non-IID Data for Indoor Localization0
Federated Learning as a Mean-Field Game0
DER Forecast using Privacy Preserving Federated Learning0
Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networks0
Federated Model Search via Reinforcement Learning0
Management of Resource at the Network Edge for Federated Learning0
Federated Learning with Downlink Device Selection0
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
Faithful Edge Federated Learning: Scalability and Privacy0
Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data0
A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points0
UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach0
Weight Divergence Driven Divide-and-Conquer Approach for Optimal Federated Learning from non-IID Data0
Federated Dynamic Spectrum Access0
Reward-Based 1-bit Compressed Federated Distillation on Blockchain0
Over-the-Air Federated Multi-Task Learning0
A Comprehensive Survey of Incentive Mechanism for Federated Learning0
Implicit Gradient Alignment in Distributed and Federated Learning0
Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approach0
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy0
Personalized Federated Learning with Contextualized Generalization0
Federated Noisy Client LearningCode0
Privacy Threats Analysis to Secure Federated Learning0
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning0
FLRA: A Reference Architecture for Federated Learning Systems0
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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