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 37013750 of 6771 papers

TitleStatusHype
When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection0
FLIPS: Federated Learning using Intelligent Participant Selection0
Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data MiningCode0
Private Federated Learning with Dynamic Power Control via Non-Coherent Over-the-Air Computation0
Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics0
Label Inference Attacks against Node-level Vertical Federated GNNs0
Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategies0
SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspection0
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity0
Federated Representation Learning for Automatic Speech Recognition0
Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity0
Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives0
Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning0
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy0
Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation0
Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Review0
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation0
Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings0
Federated Learning for Data and Model Heterogeneity in Medical Imaging0
Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup0
Shuffled Differentially Private Federated Learning for Time Series Data Analytics0
Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain Based on Federated Learning0
UPFL: Unsupervised Personalized Federated Learning towards New ClientsCode0
Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness0
SemiSFL: Split Federated Learning on Unlabeled and Non-IID DataCode0
The Applicability of Federated Learning to Official Statistics0
Samplable Anonymous Aggregation for Private Federated Data Analysis0
Brain Age Estimation Using Structural MRI: A Clustered Federated Learning ApproachCode0
HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning0
Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings0
Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space0
EdgeConvEns: Convolutional Ensemble Learning for Edge Intelligence0
Mitigating Cross-client GANs-based Attack in Federated Learning0
FedDRL: A Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learning0
Scaff-PD: Communication Efficient Fair and Robust Federated Learning0
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning0
SplitFed resilience to packet loss: Where to split, that is the question0
Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning0
Towards Bridging the FL Performance-Explainability Trade-Off: A Trustworthy 6G RAN Slicing Use-Case0
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
MAS: Towards Resource-Efficient Federated Multiple-Task Learning0
Project Florida: Federated Learning Made Easy0
FedAutoMRI: Federated Neural Architecture Search for MR Image Reconstruction0
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
Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning0
Private Federated Learning with Autotuned Compression0
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency0
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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