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 54015450 of 6771 papers

TitleStatusHype
A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT0
A Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data0
A novel parameter decoupling approach of personalized federated learning for image analysis0
A Novel Pearson Correlation-Based Merging Algorithm for Robust Distributed Machine Learning with Heterogeneous Data0
A Novel Privacy-Preserved Recommender System Framework based on Federated Learning0
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies0
Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing0
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices0
A Parameter Aggregation Strategy on Personalized Federated Learning0
A Payload Optimization Method for Federated Recommender Systems0
A Peer-to-peer Federated Continual Learning Network for Improving CT Imaging from Multiple Institutions0
A Penalty-Based Method for Communication-Efficient Decentralized Bilevel Programming0
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection0
A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy0
Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments0
A Post-Processing-Based Fair Federated Learning Framework0
APPFLChain: A Privacy Protection Distributed Artificial-Intelligence Architecture Based on Federated Learning and Consortium Blockchain0
Application of federated learning in manufacturing0
Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Model0
Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals0
Application of Homomorphic Encryption in Medical Imaging0
Applications of Federated Learning in IoT for Hyper Personalisation0
Applied Federated Learning: Architectural Design for Robust and Efficient Learning in Privacy Aware Settings0
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study0
Approximate and Weighted Data Reconstruction Attack in Federated Learning0
A Practical Cross-Device Federated Learning Framework over 5G Networks0
APRIL: Finding the Achilles' Heel on Privacy for Vision Transformers0
A Primal-Dual Algorithm for Hybrid Federated Learning0
A Principled Approach to Data Valuation for Federated Learning0
A privacy-preserving, distributed and cooperative FCM-based learning approach for cancer research0
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis0
A Privacy-Preserving Federated Learning Approach for Kernel methods0
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy0
A Privacy-Preserving Indoor Localization System based on Hierarchical Federated Learning0
A Privacy Preserving System for Movie Recommendations Using Federated Learning0
AquaFeL-PSO: A Monitoring System for Water Resources using Autonomous Surface Vehicles based on Multimodal PSO and Federated Learning0
A Quality-of-Service Compliance System using Federated Learning and Optimistic Rollups0
A Quantitative Metric for Privacy Leakage in Federated Learning0
A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression0
AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy0
A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning0
Architectural Blueprint For Heterogeneity-Resilient Federated Learning0
Architectural Patterns for the Design of Federated Learning Systems0
Architecture Agnostic Federated Learning for Neural Networks0
FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning0
A Real-time Contribution Measurement Method for Participants in Federated Learning0
A Reinforcement Learning-Based Approach to Graph Discovery in D2D-Enabled Federated Learning0
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments0
A Review of Federated Learning in Energy Systems0
A review of Federated Learning in Intrusion Detection Systems for IoT0
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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