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 9511000 of 6771 papers

TitleStatusHype
Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning0
Modeling Inter-Intra Heterogeneity for Graph Federated LearningCode0
Information-Geometric Barycenters for Bayesian Federated Learning0
Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in Federated Learning0
TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning0
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training0
FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated LearningCode0
UA-PDFL: A Personalized Approach for Decentralized Federated Learning0
ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes0
Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks0
Predicting Survival of Hemodialysis Patients using Federated Learning0
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and RegressionCode0
ExclaveFL: Providing Transparency to Federated Learning using Exclaves0
Client-Side Patching against Backdoor Attacks in Federated Learning0
Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series DataCode0
Deep Learning Model Security: Threats and Defenses0
Federated Foundation Models on Heterogeneous Time SeriesCode1
Predicting Quality of Video Gaming Experience Using Global-Scale Telemetry Data and Federated Learning0
Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph GenerationCode0
Federated In-Context LLM Agent Learning0
dsLassoCov: a federated machine learning approach incorporating covariate control0
Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation0
Learn How to Query from Unlabeled Data Streams in Federated LearningCode0
A Tutorial of Personalized Federated Recommender Systems: Recent Advances and Future Directions0
How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?0
Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction0
How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?0
Optimizing Personalized Federated Learning through Adaptive Layer-Wise LearningCode1
A New Federated Learning Framework Against Gradient Inversion AttacksCode1
Hierarchical Split Federated Learning: Convergence Analysis and System Optimization0
Learnable Sparse Customization in Heterogeneous Edge ComputingCode0
Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation0
Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions0
Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning0
A Framework for Privacy-Preserving in IoV Using Federated Learning With Differential Privacy0
Sequential Compression Layers for Efficient Federated Learning in Foundational Models0
A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications0
FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis0
H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications0
Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks0
Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directions0
FedRBE -- a decentralized privacy-preserving federated batch effect correction tool for omics data based on limma0
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge DevicesCode1
Upcycling Noise for Federated Unlearning0
One-shot Federated Learning via Synthetic Distiller-Distillate CommunicationCode1
NebulaFL: Effective Asynchronous Federated Learning for JointCloud Computing0
Privacy Drift: Evolving Privacy Concerns in Incremental Learning0
A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities0
Communication Compression for Distributed Learning without Control Variates0
FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated LearningCode0
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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