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 11511200 of 6771 papers

TitleStatusHype
Federated Noisy Client LearningCode0
FedTune: Automatic Tuning of Federated Learning Hyper-Parameters from System PerspectiveCode0
Automatic Structured Pruning for Efficient Architecture in Federated LearningCode0
Adaptive Federated Learning in Resource Constrained Edge Computing SystemsCode0
Federated Optimization for Heterogeneous NetworksCode0
Federated Stain Normalization for Computational PathologyCode0
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With SupplementCode0
Federated Hybrid Model Pruning through Loss Landscape ExplorationCode0
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental StudyCode0
Federated Neural Radiance FieldsCode0
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational EfficiencyCode0
Federated Multi-armed Bandits with PersonalizationCode0
Federated Multi-Task LearningCode0
Federated Machine Learning: Concept and ApplicationsCode0
AutoDFL: A Scalable and Automated Reputation-Aware Decentralized Federated LearningCode0
A Universal Metric of Dataset Similarity for Cross-silo Federated LearningCode0
Adaptive Expert Models for Personalization in Federated LearningCode0
A Unified Solution to Diverse Heterogeneities in One-shot Federated LearningCode0
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated LearningCode0
Federated LoRA with Sparse CommunicationCode0
A Unified Benchmark of Federated Learning with Kolmogorov-Arnold Networks for Medical ImagingCode0
Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!Code0
Federated Low-Rank Adaptation for Foundation Models: A SurveyCode0
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer InterfacesCode0
Federated Submodel Optimization for Hot and Cold Data FeaturesCode0
Federated Learning with Reduced Information Leakage and ComputationCode0
Aggregation Delayed Federated LearningCode0
AugFL: Augmenting Federated Learning with Pretrained ModelsCode0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Federated Learning with Non-IID DataCode0
Federated Learning with Only Positive LabelsCode0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
Labeling Chaos to Learning Harmony: Federated Learning with Noisy LabelsCode0
Federated Learning with Intermediate Representation RegularizationCode0
Federated Learning With Individualized Privacy Through Client SamplingCode0
Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object DetectionCode0
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
Attribute Inference Attacks for Federated Regression TasksCode0
Attentive Federated Learning for Concept Drift in Distributed 5G Edge NetworksCode0
Attention on Personalized Clinical Decision Support System: Federated Learning ApproachCode0
Attacks on fairness in Federated LearningCode0
Federated Learning with Additional Mechanisms on Clients to Reduce Communication CostsCode0
Attack-Resistant Federated Learning with Residual-based ReweightingCode0
Federated Learning with a Single Shared ImageCode0
Federated Learning via Plurality VoteCode0
ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and ReasoningCode0
A generic framework for privacy preserving deep learningCode0
Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and AnalysisCode0
Federated Learning under Partially Class-Disjoint Data via Manifold ReshapingCode0
Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on ConvergenceCode0
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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