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 41514200 of 6771 papers

TitleStatusHype
Using Federated Machine Learning in Predictive Maintenance of Jet Engines0
Using Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learning0
Utility Fairness for the Differentially Private Federated Learning0
Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning0
UTILIZING FEDERATED LEARNING AND META LEARNING FOR FEW-SHOT LEARNING ON EDGE DEVICES0
Utilizing Free Clients in Federated Learning for Focused Model Enhancement0
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection0
Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks0
VAFL: a Method of Vertical Asynchronous Federated Learning0
Value of Information and Timing-aware Scheduling for Federated Learning0
Vanishing Variance Problem in Fully Decentralized Neural-Network Systems0
Variance-Reduced Heterogeneous Federated Learning via Stratified Client Selection0
Variational Federated Multi-Task Learning0
VeFIA: An Efficient Inference Auditing Framework for Vertical Federated Collaborative Software0
VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated Learning0
VeriFi: Towards Verifiable Federated Unlearning0
Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning0
Version age-based client scheduling policy for federated learning0
A Distributed Privacy Preserving Model for the Detection of Alzheimer's Disease0
Vertical Federated Continual Learning via Evolving Prototype Knowledge0
Vertical Federated Image Segmentation0
Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks0
Vertical Federated Learning: Concepts, Advances and Challenges0
Vertical Federated Learning: A Structured Literature Review0
Vertical federated learning based on DFP and BFGS0
Vertical Federated Learning: Challenges, Methodologies and Experiments0
Vertical Federated Learning for Failure-Cause Identification in Disaggregated Microwave Networks0
Vertical Federated Learning Hybrid Local Pre-training0
Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly0
Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization0
Vertical Federated Learning: Taxonomies, Threats, and Prospects0
Vertical Federated Learning without Revealing Intersection Membership0
Vertical Semi-Federated Learning for Efficient Online Advertising0
VFedMH: Vertical Federated Learning for Training Multiple Heterogeneous Models0
Secure Vertical Federated Learning Under Unreliable Connectivity0
VFLGAN-TS: Vertical Federated Learning-based Generative Adversarial Networks for Publication of Vertically Partitioned Time-Series Data0
VFL-RPS: Relevant Participant Selection in Vertical Federated Learning0
VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning0
Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation0
Viewport Prediction, Bitrate Selection, and Beamforming Design for THz-Enabled 360° Video Streaming0
Vision Foundation Models in Medical Image Analysis: Advances and Challenges0
Vision Language Models in Medicine0
Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification0
Visual Prompt Based Personalized Federated Learning0
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning0
VLLFL: A Vision-Language Model Based Lightweight Federated Learning Framework for Smart Agriculture0
Voting-based Approaches For Differentially Private Federated Learning0
Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities0
WAFFLe: Weight Anonymized Factorization for Federated Learning0
WAFFLE: Weighted Averaging for Personalized Federated Learning0
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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