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 31513200 of 6771 papers

TitleStatusHype
Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness0
UPFL: Unsupervised Personalized Federated Learning towards New ClientsCode0
You Can Backdoor Personalized Federated LearningCode1
The Applicability of Federated Learning to Official Statistics0
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental DesignCode1
Brain Age Estimation Using Structural MRI: A Clustered Federated Learning ApproachCode0
Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image ClassificationCode1
Samplable Anonymous Aggregation for Private Federated Data Analysis0
Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings0
HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning0
Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space0
Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning0
EdgeConvEns: Convolutional Ensemble Learning for Edge Intelligence0
SplitFed resilience to packet loss: Where to split, that is the question0
FedDRL: A Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learning0
Scaff-PD: Communication Efficient Fair and Robust Federated Learning0
Mitigating Cross-client GANs-based Attack in Federated Learning0
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning0
Towards Bridging the FL Performance-Explainability Trade-Off: A Trustworthy 6G RAN Slicing Use-Case0
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical ImagingCode1
ProtoFL: Unsupervised Federated Learning via Prototypical Distillation0
Security and Privacy Issues of Federated Learning0
CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT EnvironmentCode0
Project Florida: Federated Learning Made Easy0
Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks0
Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information DecompositionCode0
Mitigating Communications Threats in Decentralized Federated Learning through Moving Target DefenseCode1
Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning0
MAS: Towards Resource-Efficient Federated Multiple-Task Learning0
FedAutoMRI: Federated Neural Architecture Search for MR Image Reconstruction0
SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning0
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model InterpolationCode1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language ProcessingCode1
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency0
Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks0
Private Federated Learning with Autotuned Compression0
Boosting Federated Learning Convergence with Prototype Regularization0
Heterogeneous Federated Learning: State-of-the-art and Research ChallengesCode1
Fairness-Aware Client Selection for Federated Learning0
Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior0
Graph Federated Learning Based on the Decentralized Framework0
Eliminating Label Leakage in Tree-Based Vertical Federated Learning0
Learner Referral for Cost-Effective Federated Learning Over Hierarchical IoT Networks0
FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated LearningCode0
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured LearningCode1
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey0
Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning0
A Federated learning model for Electric Energy management using Blockchain Technology0
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing0
Integration of Large Language Models and 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