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 48514900 of 6771 papers

TitleStatusHype
Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation0
Upcycling Noise for Federated Unlearning0
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge0
Update Compression for Deep Neural Networks on the Edge0
Update Estimation and Scheduling for Over-the-Air Federated Learning with Energy Harvesting Devices0
Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation0
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning0
Use of Federated Learning and Blockchain towards Securing Financial Services0
User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks0
User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data0
User-Centric Federated Learning0
User-Centric Federated Learning: Trading off Wireless Resources for Personalization0
User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative environments0
User Scheduling for Federated Learning Through Over-the-Air Computation0
Using adversarial images to improve outcomes of federated learning for non-IID data0
Using Decentralized Aggregation for Federated Learning with Differential Privacy0
Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?0
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
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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