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 29513000 of 6771 papers

TitleStatusHype
Federated Learning with Limited Node Labels0
Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication0
Federated Learning with LoRA Optimized DeiT and Multiscale Patch Embedding for Secure Eye Disease Recognition0
Federated Learning with Lossy Distributed Source Coding: Analysis and Optimization0
Federated Learning with Manifold Regularization and Normalized Update Reaggregation0
Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization0
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification0
Federated Learning with Multi-resolution Model Broadcast0
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization0
FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning0
Heterogeneity-Aware Client Sampling: A Unified Solution for Consistent Federated Learning0
Deep Equilibrium Models Meet Federated Learning0
Federated Learning with Noisy User Feedback0
Deep Federated Anomaly Detection for Multivariate Time Series Data0
Federated Pruning: Improving Neural Network Efficiency with Federated Learning0
Federated Learning with Nonvacuous Generalisation Bounds0
Federated Learning with Only Positive Labels by Exploring Label Correlations0
Federated Learning without Full Labels: A Survey0
Federated Learning without Revealing the Decision Boundaries0
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach0
Federated Quantum Machine Learning0
Federated Learning with Personalization Layers0
Federated Learning with Position-Aware Neurons0
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing0
Federated Learning with Privacy-Preserving Ensemble Attention Distillation0
Federated Learning with Projected Trajectory Regularization0
Federated Learning With Quantized Global Model Updates0
Federated PCA and Estimation for Spiked Covariance Matrices: Optimal Rates and Efficient Algorithm0
FedCC: Robust Federated Learning against Model Poisoning Attacks0
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency0
Federated Learning with Regularized Client Participation0
Federated Learning with Relative Fairness0
Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology0
Deep Learning Model Security: Threats and Defenses0
Federated Learning with Sample-level Client Drift Mitigation0
Federated Learning with Server Learning: Enhancing Performance for Non-IID Data0
A Federated Channel Modeling System using Generative Neural Networks0
Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy0
Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection0
Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating0
FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity0
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings0
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions0
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT0
Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection0
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets0
Federated Linear Contextual Bandits with Heterogeneous Clients0
Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing0
A review on different techniques used to combat the non-IID and heterogeneous nature of data in FL0
FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning0
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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