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 62016250 of 6771 papers

TitleStatusHype
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous ClientsCode1
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies0
Towards Bidirectional Protection in Federated Learning0
Practical One-Shot Federated Learning for Cross-Silo SettingCode1
Model-sharing Games: Analyzing Federated Learning Under Voluntary ParticipationCode1
Blind Federated Learning at the Wireless Edge with Low-Resolution ADC and DAC0
Optimal Task Assignment to Heterogeneous Federated Learning DevicesCode0
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANsCode1
A Real-time Contribution Measurement Method for Participants in Federated Learning0
Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer0
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning0
Loosely Coupled Federated Learning Over Generative Models0
Over-the-Air Federated Learning from Heterogeneous DataCode1
Federated Transfer Learning: concept and applications0
Privacy-preserving Transfer Learning via Secure Maximum Mean Discrepancy0
FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning0
Dynamic Fusion based Federated Learning for COVID-19 Detection0
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling0
When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network0
An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach0
Federated Learning for Computational Pathology on Gigapixel Whole Slide ImagesCode1
Training Production Language Models without Memorizing User Data0
Estimation of Individual Device Contributions for Incentivizing Federated Learning0
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning0
Federated Learning with Nesterov Accelerated Gradient0
FLAME: Differentially Private Federated Learning in the Shuffle ModelCode1
Distilled One-Shot Federated Learning0
Byzantine-Robust Variance-Reduced Federated Learning over Distributed Non-i.i.d. DataCode0
FedSmart: An Auto Updating Federated Learning Optimization Mechanism0
Federated Dynamic GNN with Secure Aggregation0
A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring0
Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data0
Robustness and Personalization in Federated Learning: A Unified Approach via Regularization0
A Principled Approach to Data Valuation for Federated Learning0
Private data sharing between decentralized users through the privGAN architecture0
SAPAG: A Self-Adaptive Privacy Attack From Gradients0
FLaPS: Federated Learning and Privately ScalingCode0
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient DescentCode1
Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning0
Federated Model Distillation with Noise-Free Differential Privacy0
Low-Rank Training of Deep Neural Networks for Emerging Memory Technology0
FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning0
Local and Central Differential Privacy for Robustness and Privacy in Federated Learning0
Hybrid Differentially Private Federated Learning on Vertically Partitioned Data0
Blockchain-based Federated Learning for Failure Detection in Industrial IoT0
Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services0
FedBE: Making Bayesian Model Ensemble Applicable to Federated LearningCode1
ESMFL: Efficient and Secure Models for Federated Learning0
Federated Learning for Breast Density Classification: A Real-World Implementation0
Fed-Sim: Federated Simulation for Medical Imaging0
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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