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 30513100 of 6771 papers

TitleStatusHype
Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning0
Decentralized Blockchain-based Robust Multi-agent Multi-armed Bandit0
Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networks0
A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective0
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes0
Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence GuaranteesCode0
Federated Learning Priorities Under the European Union Artificial Intelligence Act0
Time-Distributed Backdoor Attacks on Federated Spiking Learning0
Fairness and Privacy Guarantees in Federated Contextual Bandits0
Exploring Federated Self-Supervised Learning for General Purpose Audio Understanding0
Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things0
Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks0
Federated Learning with Differential Privacy0
Position Paper: Assessing Robustness, Privacy, and Fairness in Federated Learning Integrated with Foundation Models0
Training-time Neuron Alignment through Permutation Subspace for Improving Linear Mode Connectivity and Model Fusion0
Parametric Feature Transfer: One-shot Federated Learning with Foundation Models0
An Auction-based Marketplace for Model Trading in Federated Learning0
DFML: Decentralized Federated Mutual Learning0
FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation0
Privacy-Preserving Distributed Learning for Residential Short-Term Load ForecastingCode0
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated LearningCode0
Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach0
Survey of Privacy Threats and Countermeasures in Federated Learning0
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
Blockchain-enabled Trustworthy Federated Unlearning0
Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation0
FedFair^3: Unlocking Threefold Fairness in Federated Learning0
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies0
Cross-silo Federated Learning with Record-level Personalized Differential PrivacyCode0
Spectral Co-Distillation for Personalized Federated LearningCode0
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
The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness0
Decentralized Federated Learning: A Survey on Security and Privacy0
Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modality0
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning0
Federated learning with distributed fixed design quantum chips and quantum channels0
A V2X-based Privacy Preserving Federated Measuring and Learning SystemCode0
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
On Principled Local Optimization Methods for Federated Learning0
Viewport Prediction, Bitrate Selection, and Beamforming Design for THz-Enabled 360° Video Streaming0
FedRSU: Federated Learning for Scene Flow Estimation on Roadside UnitsCode0
Secure Federated Learning Approaches to Diagnosing COVID-190
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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