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 23012350 of 6771 papers

TitleStatusHype
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network0
A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency0
Comparative assessment of federated and centralized machine learning0
Accelerating Federated Learning in Heterogeneous Data and Computational Environments0
1-D CNN-Based Online Signature Verification with Federated Learning0
Inferring Communities of Interest in Collaborative Learning-based Recommender Systems0
CommunityAI: Towards Community-based Federated Learning0
Communication Trade-offs in Federated Learning of Spiking Neural Networks0
Federated Learning of Neural ODE Models with Different Iteration Counts0
A Survey of Federated Learning for Connected and Automated Vehicles0
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection0
Federated Bayesian Network Ensembles0
Communication-robust and Privacy-safe Distributed Estimation for Heterogeneous Community-level Behind-the-meter Solar Power Generation0
A Survey of Federated Evaluation in Federated Learning0
A Federated Learning Framework for Non-Intrusive Load Monitoring0
Communication Optimization in Large Scale Federated Learning using Autoencoder Compressed Weight Updates0
Communication Optimization for Decentralized Learning atop Bandwidth-limited Edge Networks0
Towards Fairness-Aware Federated Learning0
Federated Bandit: A Gossiping Approach0
A Survey for Large Language Models in Biomedicine0
Communication-Efficient Wireless Federated Fine-Tuning for Large-Scale AI Models0
ActPerFL: Active Personalized Federated Learning0
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples0
A Federated Learning Framework for Healthcare IoT devices0
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms0
Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models0
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process0
Communication-Efficient Robust Federated Learning with Noisy Labels0
A study on performance limitations in Federated Learning0
Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets0
A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example0
A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges0
Federated Automatic Differentiation0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks0
Active-Passive Federated Learning for Vertically Partitioned Multi-view Data0
Federated Anomaly Detection over Distributed Data Streams0
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data0
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection0
A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networks0
Federated Automated Feature Engineering0
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes0
Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments0
Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory0
A Stochastic Gradient Langevin Dynamics Algorithm For Noise Intrinsic Federated Learning0
Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning0
A Federated Learning-based Lightweight Network with Zero Trust for UAV Authentication0
Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients0
A State Alignment-Centric Approach to Federated System Identification: The FedAlign Framework0
Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning0
Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission0
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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