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 551600 of 6771 papers

TitleStatusHype
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision TransformersCode1
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT ImagingCode1
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
Federated Learning via Inexact ADMMCode1
Byzantine-Robust Decentralized Learning via ClippedGossipCode1
Personalized Federated Learning with Adaptive Batchnorm for HealthcareCode1
Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT NetworksCode1
Federated Learning with Diffusion Models for Privacy-Sensitive Vision TasksCode1
C2A: Client-Customized Adaptation for Parameter-Efficient Federated LearningCode1
ByzFL: Research Framework for Robust Federated LearningCode1
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept DriftCode1
CAFE: Catastrophic Data Leakage in Vertical Federated LearningCode1
Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT NetworksCode1
Can Foundation Models Help Us Achieve Perfect Secrecy?Code1
Asynchronous Federated Continual LearningCode1
CaPC Learning: Confidential and Private Collaborative LearningCode1
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot InteractionCode1
Catastrophic Data Leakage in Vertical Federated LearningCode1
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian SamplingCode1
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret SharingCode1
A Federated Data-Driven Evolutionary AlgorithmCode1
FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite OptimizationCode1
Federated Learning with Taskonomy for Non-IID DataCode1
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor SegmentationCode1
FwdLLM: Efficient FedLLM using Forward GradientCode1
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical ImagingCode1
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-offCode1
Federated nnU-Net for Privacy-Preserving Medical Image SegmentationCode1
A federated graph neural network framework for privacy-preserving personalizationCode1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and ComparisonCode1
Federated Reconstruction: Partially Local Federated LearningCode1
A Survey on Vulnerability of Federated Learning: A Learning Algorithm PerspectiveCode1
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated LearningCode1
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and QuantizationCode1
CoDeC: Communication-Efficient Decentralized Continual LearningCode1
Collaborative Fairness in Federated LearningCode1
Federated Transfer Learning for EEG Signal ClassificationCode1
Combating Exacerbated Heterogeneity for Robust Models in Federated LearningCode1
Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation MetricsCode1
Federated Few-shot LearningCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoTCode1
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous DataCode1
FedFly: Towards Migration in Edge-based Distributed Federated LearningCode1
Federated Foundation Models on Heterogeneous Time SeriesCode1
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance ReductionCode1
Communication-Efficient Diffusion Strategy for Performance Improvement of Federated Learning with Non-IID DataCode1
A Survey for Federated Learning Evaluations: Goals and MeasuresCode1
Auditing Privacy Defenses in Federated Learning via Generative Gradient LeakageCode1
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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