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 58015850 of 6771 papers

TitleStatusHype
Providing Differential Privacy for Federated Learning Over Wireless: A Cross-layer Framework0
Providing Location Information at Edge Networks: A Federated Learning-Based Approach0
Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains0
Proximity-based Self-Federated Learning0
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!0
Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learning0
Pseudo-Data based Self-Supervised Federated Learning for Classification of Histopathological Images0
PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy0
Public Data-Assisted Mirror Descent for Private Model Training0
QA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image Quality0
QFAL: Quantum Federated Adversarial Learning0
QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection0
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning0
QMGeo: Differentially Private Federated Learning via Stochastic Quantization with Mixed Truncated Geometric Distribution0
Quadratic Functional Encryption for Secure Training in Vertical Federated Learning0
Communication-Efficient Federated Learning With Data and Client Heterogeneity0
QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation0
Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations0
Quality monitoring of federated Covid-19 lesion segmentation0
QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning0
Quantifying the Impact of Label Noise on Federated Learning0
FedLPA: One-shot Federated Learning with Layer-Wise Posterior AggregationCode0
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed DataCode0
FedLion: Faster Adaptive Federated Optimization with Fewer CommunicationCode0
FedLWS: Federated Learning with Adaptive Layer-wise Weight ShrinkingCode0
Wind turbine condition monitoring based on intra- and inter-farm federated learningCode0
FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed LearningCode0
RVE-PFL: Robust Variational Encoder-based Personalised Federated Learning against Model Inversion AttacksCode0
Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID DataCode0
FedCCRL: Federated Domain Generalization with Cross-Client Representation LearningCode0
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP OptimizationCode0
Adaptive Guidance for Local Training in Heterogeneous Federated LearningCode0
FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation ModelsCode0
Optimization with Access to Auxiliary InformationCode0
Knowledge Distillation For Wireless Edge LearningCode0
FedKD-hybrid: Federated Hybrid Knowledge Distillation for Lithography Hotspot DetectionCode0
FedIMPUTE: Privacy-Preserving Missing Value Imputation for Multi-Site Heterogeneous Electronic Health RecordsCode0
FedICT: Federated Multi-task Distillation for Multi-access Edge ComputingCode0
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement LearningCode0
Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoTCode0
FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge InjectionCode0
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation AnalysisCode0
S2FGL: Spatial Spectral Federated Graph LearningCode0
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with SparsificationCode0
Knowledge-Injected Federated LearningCode0
KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from ServerCode0
UPFL: Unsupervised Personalized Federated Learning towards New ClientsCode0
Krum Federated Chain (KFC): Using blockchain to defend against adversarial attacks in Federated LearningCode0
FedIA: Federated Medical Image Segmentation with Heterogeneous Annotation CompletenessCode0
FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge DevicesCode0
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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