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 24012450 of 6771 papers

TitleStatusHype
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated LearningCode0
Privacy-Preserving Distributed Learning for Residential Short-Term Load ForecastingCode0
FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation0
Workflow Optimization for Parallel Split LearningCode1
Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach0
Survey of Privacy Threats and Countermeasures in Federated Learning0
MP-SL: Multihop Parallel Split LearningCode1
FedCore: Straggler-Free Federated Learning with Distributed CoresetsCode0
CNN-FL for Biotechnology Industry Empowered by Internet-of-BioNano Things and Digital Twins0
Rendering Wireless Environments Useful for Gradient Estimators: A Zero-Order Stochastic Federated Learning Method0
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection0
Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation0
Spectral Co-Distillation for Personalized Federated LearningCode0
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies0
Blockchain-enabled Trustworthy Federated Unlearning0
FedFair^3: Unlocking Threefold Fairness in Federated Learning0
Cross-silo Federated Learning with Record-level Personalized Differential PrivacyCode0
Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI0
FedGT: Federated Node Classification with Scalable Graph Transformer0
Multi-model learning by sequential reading of untrimmed videos for action recognition0
P3LS: Partial Least Squares under Privacy Preservation0
Prompt-enhanced Federated Content Representation Learning for Cross-domain RecommendationCode1
Decentralized Federated Learning: A Survey on Security and Privacy0
Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality0
The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness0
Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side DistillationCode1
Federated learning with distributed fixed design quantum chips and quantum channels0
On Principled Local Optimization Methods for Federated Learning0
A V2X-based Privacy Preserving Federated Measuring and Learning SystemCode0
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning0
Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems0
Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed0
Viewport Prediction, Bitrate Selection, and Beamforming Design for THz-Enabled 360° Video Streaming0
Secure Federated Learning Approaches to Diagnosing COVID-190
FedRSU: Federated Learning for Scene Flow Estimation on Roadside UnitsCode0
Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting0
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems0
Declarative Privacy-Preserving Inference Queries0
LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning0
TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy ClientsCode0
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning0
GI-PIP: Do We Require Impractical Auxiliary Dataset for Gradient Inversion Attacks?Code1
Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces0
Attention on Personalized Clinical Decision Support System: Federated Learning ApproachCode0
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity0
Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach0
Differentially-Private Multi-Tier Federated Learning0
FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph EnhancementCode0
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks0
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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