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 34013450 of 6771 papers

TitleStatusHype
Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence0
Rehearsal-free Federated Domain-incremental Learning0
Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering0
Reliability and Performance Assessment of Federated Learning on Clinical Benchmark Data0
Reliable and Interpretable Personalized Federated Learning0
Reliable Imputed-Sample Assisted Vertical Federated Learning0
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges0
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients0
Rendering Wireless Environments Useful for Gradient Estimators: A Zero-Order Stochastic Federated Learning Method0
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning0
REPA: Client Clustering without Training and Data Labels for Improved Federated Learning in Non-IID Settings0
Report of the Medical Image De-Identification (MIDI) Task Group -- Best Practices and Recommendations0
Representation Learning to Advance Multi-institutional Studies with Electronic Health Record Data0
Representation of Federated Learning via Worst-Case Robust Optimization Theory0
RepuNet: A Reputation System for Mitigating Malicious Clients in DFL0
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification0
Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain0
Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning0
Research on Resource Allocation for Efficient Federated Learning0
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals0
RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility in Autonomous Vehicles0
Residue-based Label Protection Mechanisms in Vertical Logistic Regression0
Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks0
Resilient Constrained Learning0
Resource Allocation for Compression-aided Federated Learning with High Distortion Rate0
Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach0
Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality0
Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression0
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search0
Resource-Aware Hierarchical Federated Learning for Video Caching in Wireless Networks0
Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networks0
Resource-Constrained Federated Learning with Heterogeneous Labels and Models0
Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles0
Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneity0
Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network0
Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data0
REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments0
Resource-Efficient Federated Multimodal Learning via Layer-wise and Progressive Training0
Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach0
Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges0
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions0
Attacks to Federated Learning: Responsive Web User Interface to Recover Training Data from User Gradients0
Rethinking Client Drift in Federated Learning: A Logit Perspective0
Rethinking Client Reweighting for Selfish Federated Learning0
Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks0
Rethinking Normalization Methods in Federated Learning0
Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation0
Rethinking Semi-Supervised Federated Learning: How to co-train fully-labeled and fully-unlabeled client imaging data0
Rethinking the initialization of Momentum in Federated Learning with Heterogeneous Data0
Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data0
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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