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 17511800 of 6771 papers

TitleStatusHype
Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction0
Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions0
Learnable Sparse Customization in Heterogeneous Edge ComputingCode0
Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation0
Hierarchical Split Federated Learning: Convergence Analysis and System Optimization0
Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning0
How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?0
A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications0
Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directions0
Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks0
H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications0
FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis0
A Framework for Privacy-Preserving in IoV Using Federated Learning With Differential Privacy0
Sequential Compression Layers for Efficient Federated Learning in Foundational Models0
FedRBE -- a decentralized privacy-preserving federated batch effect correction tool for omics data based on limma0
Upcycling Noise for Federated Unlearning0
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
Federated Automated Feature Engineering0
FedDW: Distilling Weights through Consistency Optimization in Heterogeneous Federated LearningCode0
BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoTCode0
Privacy-Preserving in Medical Image Analysis: A Review of Methods and Applications0
FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems0
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated LearningCode0
GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning0
Providing Differential Privacy for Federated Learning Over Wireless: A Cross-layer Framework0
Communication Compression for Distributed Learning without Control Variates0
Reactive Orchestration for Hierarchical Federated Learning Under a Communication Cost BudgetCode0
Learn More by Using Less: Distributed Learning with Energy-Constrained Devices0
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization0
Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains0
PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality0
Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level OptimizationCode0
Fractional Order Distributed Optimization0
Towards the efficacy of federated prediction for epidemics on networksCode0
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer InterfacesCode0
Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks0
FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed PredictionCode0
HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated Learning0
FedAH: Aggregated Head for Personalized Federated LearningCode0
Review of Mathematical Optimization in Federated Learning0
Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture0
Incentivizing Truthful Collaboration in Heterogeneous Federated Learning0
MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption0
IRS Aided Federated Learning: Multiple Access and Fundamental Tradeoff0
Gradient Inversion Attack on Graph Neural Networks0
Rethinking the initialization of Momentum in Federated Learning with Heterogeneous Data0
PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning0
Controlling Participation in Federated Learning with FeedbackCode0
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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