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 36013650 of 6771 papers

TitleStatusHype
FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic SystemsCode1
On Mitigating the Utility-Loss in Differentially Private Learning: A new Perspective by a Geometrically Inspired Kernel Approach0
Federated Kalman Filter for Secure IoT-based Device Monitoring Services0
FedIN: Federated Intermediate Layers Learning for Model Heterogeneity0
Personalized Federated Learning with Local Attention0
Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence0
A Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions0
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET DenoisingCode0
MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence0
Benchmarking FedAvg and FedCurv for Image Classification Tasks0
Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis0
Federated Learning Based Multilingual Emoji Prediction In Clean and Attack ScenariosCode0
Federated Learning from Heterogeneous Data via Controlled Bayesian Air Aggregation0
DPP-based Client Selection for Federated Learning with Non-IID Data0
FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA0
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection0
Federated Learning in MIMO Satellite Broadcast System0
Fair Federated Medical Image Segmentation via Client Contribution Estimation0
Federated Learning for Heterogeneous Bandits with Unobserved Contexts0
FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination0
Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties0
Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples0
On the Local Cache Update Rules in Streaming Federated Learning0
A Comparative Study of Federated Learning Models for COVID-19 Detection0
FedAgg: Adaptive Federated Learning with Aggregated Gradients0
Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation0
Asynchronous Online Federated Learning with Reduced Communication Requirements0
Adaptive Federated Learning via New Entropy Approach0
Neural Collapse Inspired Federated Learning with Non-iid Data0
CoDeC: Communication-Efficient Decentralized Continual LearningCode1
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature AugmentationCode0
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning0
Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks0
Green Federated Learning0
Edge Selection and Clustering for Federated Learning in Optical Inter-LEO Satellite Constellation0
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge DistillationCode0
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks0
Federated Learning without Full Labels: A Survey0
A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing0
Secure Federated Learning for Cognitive Radio Sensing0
Federated Learning for Metaverse: A Survey0
AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT0
Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging0
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence0
Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks0
FedGH: Heterogeneous Federated Learning with Generalized Global HeaderCode2
FS-Real: Towards Real-World Cross-Device Federated Learning0
Re-thinking Federated Active Learning based on Inter-class DiversityCode1
Prototype Helps Federated Learning: Towards Faster Convergence0
Delay-Aware Hierarchical 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