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

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Showing 3601–3650 of 6771 papers

TitleStatusHype
On the Practicality of Differential Privacy in Federated Learning by Tuning Iteration Times—0
On the Role of Server Momentum in Federated Learning—0
On the Stability Analysis of Open Federated Learning Systems—0
On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks—0
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data—0
Ontology- and LLM-based Data Harmonization for Federated Learning in Healthcare—0
Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare—0
OpenFL: An open-source framework for Federated Learning—0
Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning—0
Open-Source LLM-Driven Federated Transformer for Predictive IoV Management—0
Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications—0
Optimal Batch Allocation for Wireless Federated Learning—0
Optimal Client Sampling in Federated Learning with Client-Level Heterogeneous Differential Privacy—0
Optimal Complexity in Non-Convex Decentralized Learning over Time-Varying Networks—0
Optimal Federated Learning for Functional Mean Estimation under Heterogeneous Privacy Constraints—0
Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints—0
Optimal Gradient Compression for Distributed and Federated Learning—0
Optimal Importance Sampling for Federated Learning—0
Optimal Model Averaging: Towards Personalized Collaborative Learning—0
Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing—0
Optimal Rate Adaption in Federated Learning with Compressed Communications—0
Optimal Rates for O(1)-Smooth DP-SCO with a Single Epoch and Large Batches—0
Optimal Strategies for Federated Learning Maintaining Client Privacy—0
Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learning—0
Optimisation of federated learning settings under statistical heterogeneity variations—0
Optimising Communication Overhead in Federated Learning Using NSGA-II—0
Optimization-Based GenQSGD for Federated Edge Learning—0
Optimization Design for Federated Learning in Heterogeneous 6G Networks—0
Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systems—0
Optimizing Asynchronous Federated Learning: A~Delicate Trade-Off Between Model-Parameter Staleness and Update Frequency—0
Boosting Federated Learning with FedEntOpt: Mitigating Label Skew by Entropy-Based Client Selection—0
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels—0
Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling—0
Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks—0
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning—0
Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descent—0
Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks—0
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization—0
Optimizing Split Points for Error-Resilient SplitFed Learning—0
Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems—0
Order Optimal Bounds for One-Shot Federated Learning over non-Convex Loss Functions—0
Orthogonal Calibration for Asynchronous Federated Learning—0
Overcoming Catastrophic Forgetting in Federated Class-Incremental Learning via Federated Global Twin Generator—0
Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review—0
Overcoming Forgetting in Federated Learning on Non-IID Data—0
Overcoming label shift in targeted federated learning—0
Overlay-based Decentralized Federated Learning in Bandwidth-limited Networks—0
Overpredictive Signal Analytics in Federated Learning: Algorithms and Analysis—0
Over-the-Air Aggregation for Federated Learning: Waveform Superposition and Prototype Validation—0
Over-The-Air Clustered Wireless Federated Learning—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