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 901950 of 6771 papers

TitleStatusHype
Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical DiagnosisCode0
Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks0
Effective and secure federated online learning to rank0
Optimal Federated Learning for Functional Mean Estimation under Heterogeneous Privacy Constraints0
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated LearningCode1
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach0
FedGIG: Graph Inversion from Gradient in Federated Learning0
An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack0
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis0
Addressing Spatial-Temporal Data Heterogeneity in Federated Continual Learning via Tail Anchor0
GeFL: Model-Agnostic Federated Learning with Generative Models0
Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection in Resource-Limited Settings0
Better Knowledge Enhancement for Privacy-Preserving Cross-Project Defect Prediction0
FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks0
Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine LearningCode0
Exploiting Label Skewness for Spiking Neural Networks in Federated Learning0
FedTLU: Federated Learning with Targeted Layer Updates0
FedCross: Intertemporal Federated Learning Under Evolutionary Games0
Data value estimation on private gradients0
Label Privacy in Split Learning for Large Models with Parameter-Efficient TrainingCode0
Federal Learning Framework for Quality Evaluation of Blastomere Cleavage0
Fed-ZOE: Communication-Efficient Over-the-Air Federated Learning via Zeroth-Order Estimation0
FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation0
Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions0
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation0
DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game0
AutoRank: MCDA Based Rank Personalization for LoRA-Enabled Distributed Learning0
The Impact of Cut Layer Selection in Split Federated Learning0
fluke: Federated Learning Utility frameworK for Experimentation and researchCode2
Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration0
FLAMe: Federated Learning with Attention Mechanism using Spatio-Temporal Keypoint Transformers for Pedestrian Fall Detection in Smart Cities0
Summary of Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images0
LoLaFL: Low-Latency Federated Learning via Forward-only Propagation0
Robust Federated Learning in the Face of Covariate Shift: A Magnitude Pruning with Hybrid Regularization Framework for Enhanced Model Aggregation0
FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning0
SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated LearningCode0
Covariances for Free: Exploiting Mean Distributions for Federated Learning with Pre-Trained ModelsCode0
FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning0
On the Robustness of Distributed Machine Learning against Transfer AttacksCode0
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data0
Federated t-SNE and UMAP for Distributed Data Visualization0
Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence0
Federated Unlearning Model Recovery in Data with Skewed Label Distributions0
Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models0
SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated LearningCode1
Building Gradient Bridges: Label Leakage from Restricted Gradient Sharing in Federated Learning0
Concurrent vertical and horizontal federated learning with fuzzy cognitive maps0
F-RBA: A Federated Learning-based Framework for Risk-based Authentication0
Just a Simple Transformation is Enough for Data Protection in Vertical Federated LearningCode0
Efficiently Achieving Secure Model Training and Secure Aggregation to Ensure Bidirectional Privacy-Preservation in 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