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 60516100 of 6771 papers

TitleStatusHype
Provably Personalized and Robust Federated LearningCode0
SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated LearningCode0
Device-Wise Federated Network PruningCode0
PeFLL: Personalized Federated Learning by Learning to LearnCode0
CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AICode0
A deep cut into Split Federated Self-supervised LearningCode0
Federated Neural Radiance FieldsCode0
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated LearningCode0
F3: Fair and Federated Face Attribute Classification with Heterogeneous DataCode0
Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information DecompositionCode0
FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter UpdateCode0
FAIR-FATE: Fair Federated Learning with MomentumCode0
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With SupplementCode0
FADAS: Towards Federated Adaptive Asynchronous OptimizationCode0
The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian SketchesCode0
Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & AnalyticsCode0
Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated LearningCode0
FedStale: leveraging stale client updates in federated learningCode0
FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness ReweightingCode0
Split learning for health: Distributed deep learning without sharing raw patient dataCode0
FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein EstimatorCode0
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental StudyCode0
LoGoFair: Post-Processing for Local and Global Fairness in Federated LearningCode0
Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated LearningCode0
Decoupled Subgraph Federated LearningCode0
FedStyle: Style-Based Federated Learning Crowdsourcing Framework for Art CommissionsCode0
CollaFuse: Collaborative Diffusion ModelsCode0
Long-Short History of Gradients is All You Need: Detecting Malicious and Unreliable Clients in Federated LearningCode0
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous DataCode0
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central ServerCode0
SCOTCH: An Efficient Secure Computation Framework for Secure AggregationCode0
SDBA: A Stealthy and Long-Lasting Durable Backdoor Attack in Federated LearningCode0
PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender SystemCode0
VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated LearningCode0
FedSysID: A Federated Approach to Sample-Efficient System IdentificationCode0
Federated Multi-Task LearningCode0
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational EfficiencyCode0
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated LearningCode0
Federated Multi-armed Bandits with PersonalizationCode0
Loss Tolerant Federated LearningCode0
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer InterfacesCode0
Federated Machine Learning: Concept and ApplicationsCode0
Lossy Gradient Compression: How Much Accuracy Can One Bit Buy?Code0
FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?Code0
Concealing Sensitive Samples against Gradient Leakage in Federated LearningCode0
Collaborative Unsupervised Visual Representation Learning from Decentralized DataCode0
Collaborative Training of Medical Artificial Intelligence Models with non-uniform LabelsCode0
Federated Low-Rank Adaptation for Foundation Models: A SurveyCode0
Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge AnchorCode0
QBI: Quantile-Based Bias Initialization for Efficient Private Data Reconstruction in Federated LearningCode0
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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