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 39514000 of 6771 papers

TitleStatusHype
Federated Composite Saddle Point Optimization0
Federated Neural Compression Under Heterogeneous Data0
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning0
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization0
Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks0
Local SGD Accelerates Convergence by Exploiting Second Order Information of the Loss Function0
Theoretically Principled Federated Learning for Balancing Privacy and Utility0
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training0
Stochastic Unrolled Federated LearningCode0
Fair Differentially Private Federated Learning Framework0
Federated Variational Inference: Towards Improved Personalization and Generalization0
CorrFL: Correlation-based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT EnvironmentCode0
Explicit Personalization and Local Training: Double Communication Acceleration in Federated LearningCode0
Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization0
When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User InteractionsCode0
Federated Learning of Medical Concepts Embedding using BEHRTCode0
One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes0
Privacy in Multimodal Federated Human Activity Recognition0
Can Public Large Language Models Help Private Cross-device Federated Learning?0
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection0
PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy0
Trustworthy Federated Learning: A Survey0
Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning MethodsCode0
Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participation0
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models0
Efficient Vertical Federated Learning with Secure Aggregation0
Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination0
Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages0
Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clipping0
Mitigating Group Bias in Federated Learning: Beyond Local FairnessCode0
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime0
Faster Federated Learning with Decaying Number of Local SGD Steps0
Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients0
Trustworthy Privacy-preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 with Blockchain0
Smart Policy Control for Securing Federated Learning Management System0
A novel parameter decoupling approach of personalized federated learning for image analysis0
Federated Learning over Harmonized Data Silos0
Quadratic Functional Encryption for Secure Training in Vertical Federated Learning0
FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning0
FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments0
Adaptive Federated Pruning in Hierarchical Wireless Networks0
Privacy-Preserving Taxi-Demand Prediction Using Federated Learning0
A Survey of Federated Evaluation in Federated Learning0
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling0
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous DataCode0
A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction0
Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus0
Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning0
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems0
Multi-Tier Client Selection for Mobile Federated Learning Networks0
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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