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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2151–2200 of 6771 papers

TitleStatusHype
A Framework for Double-Blind Federated Adaptation of Foundation Models—0
Convergence Analysis of Sequential Split Learning on Heterogeneous Data—0
Convergence Analysis of Split Federated Learning on Heterogeneous Data—0
Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations—0
Asynchronous Collaborative Learning Across Data Silos—0
Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clipping—0
AFLGuard: Byzantine-robust Asynchronous Federated Learning—0
Accelerating Federated Split Learning via Local-Loss-Based Training—0
Convergence Analysis of Federated Learning Methods Using Backward Error Analysis—0
Convergence Analysis and System Design for Federated Learning over Wireless Networks—0
Asynchronous Byzantine Federated Learning—0
Controlling Federated Learning for Covertness—0
AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models—0
Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI—0
FedCliP: Federated Learning with Client Pruning—0
FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling—0
FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learning—0
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models—0
FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning—0
Controlled privacy leakage propagation throughout overlapping grouped learning—0
Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representations—0
AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms—0
Contribution Evaluation in Federated Learning: Examining Current Approaches—0
Contrastive Re-localization and History Distillation in Federated CMR Segmentation—0
A First Order Meta Stackelberg Method for Robust Federated Learning—0
FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning—0
Contrastive Federated Learning with Tabular Data Silos—0
Contrastive encoder pre-training-based clustered federated learning for heterogeneous data—0
Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation—0
Contractive error feedback for gradient compression—0
CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks—0
Can Federated Learning Save The Planet?—0
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning—0
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features—0
Continuous-Time Analysis of Federated Averaging—0
Asymmetrical Vertical Federated Learning—0
Continual Learning for Smart City: A Survey—0
Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification—0
Asymmetrically Decentralized Federated Learning—0
A first look into the carbon footprint of federated learning—0
Continual Horizontal Federated Learning for Heterogeneous Data—0
Continual Distributed Learning for Crisis Management—0
Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective—0
Continual Deep Reinforcement Learning for Decentralized Satellite Routing—0
Contextual Stochastic Bilevel Optimization—0
Adapting MLOps for Diverse In-Network Intelligence in 6G Era: Challenges and Solutions—0
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air—0
Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing—0
Contextual Combinatorial Multi-output GP Bandits with Group Constraints—0
A Survey on Vertical Federated Learning: From a Layered Perspective—0
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All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75—Unverified
2SiloBN + SAMmIoU49.1—Unverified
3SiloBNmIoU45.96—Unverified
4FedSAM + SWAmIoU43.42—Unverified
5FedASAM + SWAmIoU43.02—Unverified
6FedAvg + SWAmIoU42.48—Unverified
7FedASAMmIoU42.27—Unverified
8FedSAMmIoU41.22—Unverified
9FedAvgmIoU38.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32—Unverified
2FedSAM + SWAAcc@1-1262Clients68.12—Unverified
3FedAvg + SWAAcc@1-1262Clients67.52—Unverified
4FedASAMAcc@1-1262Clients64.23—Unverified
5FedSAMAcc@1-1262Clients63.72—Unverified
6FedAvgAcc@1-1262Clients61.91—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64—Unverified
2FedASAMACC@1-100Clients39.76—Unverified
3FedSAM + SWAACC@1-100Clients39.51—Unverified
4FedSAMACC@1-100Clients36.93—Unverified
5FedAvgACC@1-100Clients36.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62—Unverified
2FedASAMACC@1-100Clients40.81—Unverified
3FedSAM + SWAACC@1-100Clients39.24—Unverified
4FedAvgACC@1-100Clients38.59—Unverified
5FedSAMACC@1-100Clients38.56—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72—Unverified
2FedSAM + SWAACC@1-100Clients46.76—Unverified
3FedASAMACC@1-100Clients46.58—Unverified
4FedSAMACC@1-100Clients44.84—Unverified
5FedAvgACC@1-100Clients41.27—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27—Unverified
2FedASAMACC@1-100Clients47.78—Unverified
3FedSAM + SWAACC@1-100Clients46.47—Unverified
4FedSAMACC@1-100Clients46.05—Unverified
5FedAvgACC@1-100Clients42.17—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17—Unverified
2FedSAM + SWAACC@1-100Clients47.96—Unverified
3FedASAMACC@1-100Clients45.61—Unverified
4FedSAMACC@1-100Clients44.73—Unverified
5FedAvgACC@1-100Clients40.43—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01—Unverified
2FedSAM + SWAACC@1-100Clients39.3—Unverified
3FedASAMACC@1-100Clients36.04—Unverified
4FedSAMACC@1-100Clients31.04—Unverified
5FedAvgACC@1-100Clients30.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97—Unverified
2FedASAM + SWAACC@1-100Clients54.79—Unverified
3FedSAM + SWAACC@1-100Clients53.67—Unverified
4FedSAMACC@1-100Clients53.39—Unverified
5FedAvgACC@1-100Clients50.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5—Unverified
2FedSAM + SWAACC@1-100Clients54.36—Unverified
3FedASAM + SWAACC@1-100Clients54.1—Unverified
4FedSAMACC@1-100Clients53.97—Unverified
5FedAvgACC@1-100Clients50.66—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81—Unverified
2FedSAMACC@1-100Clients54.01—Unverified
3FedSAM + SWAACC@1-100Clients53.9—Unverified
4FedASAM + SWAACC@1-100Clients53.86—Unverified
5FedAvgACC@1-100Clients49.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2—Unverified