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 52015250 of 6771 papers

TitleStatusHype
Advancing IIoT with Over-the-Air Federated Learning: The Role of Iterative Magnitude Pruning0
Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review0
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond0
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization0
ADVENT: Attack/Anomaly Detection in VANETs0
Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things0
Adversarial Collaborative Learning on Non-IID Features0
Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoT0
Adversarial Predictions of Data Distributions Across Federated Internet-of-Things Devices0
Adversarial Representation Sharing: A Quantitative and Secure Collaborative Learning Framework0
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning0
Adversarial training in communication constrained federated learning0
AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices0
Aegis: A Trusted, Automatic and Accurate Verification Framework for Vertical Federated Learning0
AFAFed -- Protocol analysis0
A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction0
A Fair Federated Learning Framework With Reinforcement Learning0
A Hybrid Architecture for Federated and Centralized Learning0
A Fast Blockchain-based Federated Learning Framework with Compressed Communications0
AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning0
AFed: Algorithmic Fair Federated Learning0
A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities0
A Federated Approach to Predict Emojis in Hindi Tweets0
A Federated Channel Modeling System using Generative Neural Networks0
A Federated Deep Learning Framework for Cell-Free RSMA Networks0
A Federated F-score Based Ensemble Model for Automatic Rule Extraction0
A Federated Learning Approach for Mobile Packet Classification0
A Federated Learning Approach to Anomaly Detection in Smart Buildings0
A Federated Learning Approach to Privacy Preserving Offensive Language Identification0
A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction0
A Federated Learning-based Lightweight Network with Zero Trust for UAV Authentication0
A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networks0
A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges0
A Federated Learning Framework for Healthcare IoT devices0
A Federated Learning Framework for Non-Intrusive Load Monitoring0
A Federated Deep Learning Framework for Privacy Preservation and Communication Efficiency0
A Federated Learning Framework for Stenosis Detection0
A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning0
A Federated learning model for Electric Energy management using Blockchain Technology0
A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers0
A Federated Learning Scheme for Neuro-developmental Disorders: Multi-Aspect ASD Detection0
A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation0
A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures0
A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs0
A few-shot Label Unlearning in Vertical Federated Learning0
Affect-driven Ordinal Engagement Measurement from Video0
A first look into the carbon footprint of federated learning0
Can Federated Learning Save The Planet?0
A First Order Meta Stackelberg Method for Robust 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