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 3501–3550 of 6771 papers

TitleStatusHype
Differentially Private Multi-Site Treatment Effect Estimation—0
FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication—0
Secure Decentralized Learning with Blockchain—0
Federated Learning with Reduced Information Leakage and ComputationCode0
On the Convergence of Federated Averaging under Partial Participation for Over-parameterized Neural Networks—0
Find Your Optimal Assignments On-the-fly: A Holistic Framework for Clustered Federated Learning—0
Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning—0
Text-driven Prompt Generation for Vision-Language Models in Federated Learning—0
Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications—0
Towards Scalable Wireless Federated Learning: Challenges and Solutions—0
Decentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization—0
Asymmetrically Decentralized Federated Learning—0
Federated Self-Supervised Learning of Monocular Depth Estimators for Autonomous Vehicles—0
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning—0
The Role of Federated Learning in a Wireless World with Foundation Models—0
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification—0
Utilizing Free Clients in Federated Learning for Focused Model Enhancement—0
PrIeD-KIE: Towards Privacy Preserved Document Key Information Extraction—0
Over-the-Air Federated Learning with Compressed Sensing: Is Sparsification Necessary?—0
Towards Understanding Generalization and Stability Gaps between Centralized and Decentralized Federated Learning—0
Solving a Class of Non-Convex Minimax Optimization in Federated LearningCode0
Recent Methodological Advances in Federated Learning for Healthcare—0
Heterogeneous Federated Learning Using Knowledge Codistillation—0
Learning to Prompt Your Domain for Vision-Language Models—0
Inclusive Data Representation in Federated Learning: A Novel Approach Integrating Textual and Visual Prompt—0
Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework—0
FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent—0
Digital Ethics in Federated Learning—0
Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning—0
Federated Conditional Stochastic Optimization—0
zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning—0
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly—0
Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance—0
Federated Wasserstein Distance—0
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks—0
FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models—0
Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things—0
Federated K-means Clustering—0
Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning—0
Towards Understanding Adversarial Transferability in Federated Learning—0
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI ModelsCode0
Intelligent Client Selection for Federated Learning using Cellular AutomataCode0
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning—0
Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning—0
FedLPA: One-shot Federated Learning with Layer-Wise Posterior AggregationCode0
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter CollaborationCode0
Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation—0
FedZeN: Towards superlinear zeroth-order federated learning via incremental Hessian estimation—0
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping—0
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through 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