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 25512600 of 6771 papers

TitleStatusHype
Contrastive Re-localization and History Distillation in Federated CMR Segmentation0
A Secure Federated Learning Framework for Residential Short Term Load Forecasting0
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models0
Contribution Evaluation in Federated Learning: Examining Current Approaches0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation0
Federated Functional Gradient Boosting0
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework0
Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients0
Fed-DART and FACT: A solution for Federated Learning in a production environment0
Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks0
Federated Generative Adversarial Learning0
Federated Generative Learning with Foundation Models0
Federated Generative Privacy0
Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets0
Federated GNNs for EEG-Based Stroke Assessment0
Federated Gradient Matching Pursuit0
Federated Graph-based Networks with Shared Embedding0
FedDAR: Federated Domain-Aware Representation Learning0
A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications0
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning0
Federated Graph Learning -- A Position Paper0
Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks0
Federated Graph Learning with Adaptive Importance-based Sampling0
Convergence Analysis of Sequential Split Learning on Heterogeneous Data0
A Federated Learning Approach for Mobile Packet Classification0
Federated Graph Representation Learning using Self-Supervision0
A Benchmark for Federated Hetero-Task Learning0
Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control0
Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcare0
Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks0
Federated Hyperdimensional Computing0
Convergence of Federated Learning over a Noisy Downlink0
Accelerating Federated Edge Learning via Topology Optimization0
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing0
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy0
Federated In-Context LLM Agent Learning0
Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration0
Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration0
Federated Instrumental Variable Analysis via Federated Generalized Method of Moments0
Convergence Rate Maximization for Split Learning-based Control of EMG Prosthetic Devices0
Federated Deep Learning for Intrusion Detection in IoT Networks0
Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems0
Federated Kalman Filter for Secure IoT-based Device Monitoring Services0
Federated K-means Clustering0
FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging0
FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning0
Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing0
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients0
Federated Koopman-Reservoir Learning for Large-Scale Multivariate Time-Series Anomaly Detection0
Communication-Efficient Federated Learning with Sketching0
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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