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 12011250 of 6771 papers

TitleStatusHype
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational EfficiencyCode0
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental StudyCode0
Federated Machine Learning: Concept and ApplicationsCode0
Federated LoRA with Sparse CommunicationCode0
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 Optimization for Heterogeneous NetworksCode0
Federated Unlearning via Class-Discriminative PruningCode0
Federated Learning with Reduced Information Leakage and ComputationCode0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Federated Learning with Only Positive LabelsCode0
Federated Learning with Non-IID DataCode0
Adaptive Client Selection with Personalization for Communication Efficient 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
A Framework for testing Federated Learning algorithms using an edge-like environmentCode0
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning FrameworkCode0
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit FeedbackCode0
Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault DiagnosisCode0
Asynchronous Federated OptimizationCode0
Federated Learning via Plurality VoteCode0
Federated Learning with Additional Mechanisms on Clients to Reduce Communication CostsCode0
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated LearningCode0
Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on ConvergenceCode0
Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine LearningCode0
Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and AnalysisCode0
Federated Learning with a Single Shared ImageCode0
Federated Learning Under Attack: Exposing Vulnerabilities through Data Poisoning Attacks in Computer NetworksCode0
Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome TreatmentCode0
Federated Learning: Challenges, Methods, and Future DirectionsCode0
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and ProtectionCode0
Federated Learning under Partially Class-Disjoint Data via Manifold ReshapingCode0
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism DesignCode0
AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight AggregationCode0
Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated LearningCode0
Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical DiagnosisCode0
Federated Learning of Large Models at the Edge via Principal Sub-Model TrainingCode0
Federated Learning in Unreliable and Resource-Constrained Cellular Wireless NetworksCode0
Accelerating Federated Learning with a Global Biased OptimiserCode0
Federated Learning Meets Fairness and Differential PrivacyCode0
A Field Guide to Federated OptimizationCode0
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic DataCode0
Federated Causal Inference from Observational DataCode0
Federated Learning of Medical Concepts Embedding using BEHRTCode0
Federated Learning Hyper-Parameter Tuning from a System PerspectiveCode0
Federated Learning in ASR: Not as Easy as You ThinkCode0
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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