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 28012850 of 6771 papers

TitleStatusHype
Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G0
Federated Learning in Wireless Networks via Over-the-Air Computations0
Data Poisoning Attacks on Federated Machine Learning0
Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacy0
Federated Learning in MIMO Satellite Broadcast System0
Federated learning in low-resource settings: A chest imaging study in Africa -- Challenges and lessons learned0
Federated Learning Meets Natural Language Processing: A Survey0
Federated Learning Method for Preserving Privacy in Face Recognition System0
Federated Learning -- Methods, Applications and beyond0
Federated learning model for predicting major postoperative complications0
Federated Learning in IoT: a Survey from a Resource-Constrained Perspective0
Federated Learning of a Mixture of Global and Local Models0
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems0
Data Overvaluation Attack and Truthful Data Valuation in Federated Learning0
Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement0
Adaptive Distillation for Decentralized Learning from Heterogeneous Clients0
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization0
Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review0
Federated learning in food research0
Data Obfuscation through Latent Space Projection (LSP) for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection0
Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting0
Federated Learning of N-gram Language Models0
Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data0
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification0
Auction Based Clustered Federated Learning in Mobile Edge Computing System0
Federated Learning Incentive Mechanism under Buyers' Auction Market0
Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models0
Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization0
Federated Learning on Edge Sensing Devices: A Review0
Federated Learning in Adversarial Settings0
Data-Heterogeneous Hierarchical Federated Learning with Mobility0
AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT0
A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control0
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning0
Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity0
Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations0
A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals0
Federated Learning on Riemannian Manifolds0
Federated Learning: From Theory to Practice0
Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles0
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory0
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach0
Federated Learning: Opportunities and Challenges0
Federated Learning Optimization: A Comparative Study of Data and Model Exchange Strategies in Dynamic Networks0
Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategies0
Federated learning-outcome prediction with multi-layer privacy protection0
Data-Free Evaluation of User Contributions in Federated Learning0
Modeling and Analysis of Intermittent Federated Learning Over Cellular-Connected UAV Networks0
Federated Learning over Coupled Graphs0
A Two-Stage CAE-Based Federated Learning Framework for Efficient Jamming Detection in 5G 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