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 32513300 of 6771 papers

TitleStatusHype
Federated Object Detection for Quality Inspection in Shared Production0
Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification0
Federated Ensemble YOLOv5 -- A Better Generalized Object Detection Algorithm0
Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures0
A Survey on Blockchain-Based Federated Learning and Data Privacy0
Elastically-Constrained Meta-Learner for Federated Learning0
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation0
Momentum Benefits Non-IID Federated Learning Simply and Provably0
A Distributed Computation Model Based on Federated Learning Integrates Heterogeneous models and Consortium Blockchain for Solving Time-Varying Problems0
NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID DataCode0
Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization0
Frequency Modulation Aggregation for Federated Learning0
Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare0
Federated Generative Learning with Foundation Models0
Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous DrivingCode0
Quantum Federated Learning: Analysis, Design and Implementation Challenges0
FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer0
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions0
Towards Sybil Resilience in Decentralized Learning0
Federated Learning on Non-iid Data via Local and Global Distillation0
Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing0
FeSViBS: Federated Split Learning of Vision Transformer with Block SamplingCode1
Medical Federated Model with Mixture of Personalized and Sharing ComponentsCode0
Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusion0
FedSampling: A Better Sampling Strategy for Federated Learning0
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off0
A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks0
Exploring Data Redundancy in Real-world Image Classification through Data SelectionCode0
Private Networked Federated Learning for Nonsmooth Objectives0
An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices0
Blockchain-based Federated Learning for Decentralized Energy Management Systems0
A First Order Meta Stackelberg Method for Robust Federated Learning0
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning0
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneityCode0
Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition0
Reinforcement Federated Learning Method Based on Adaptive OPTICS Clustering0
Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection0
Adaptive Compression in Federated Learning via Side InformationCode0
An Efficient Virtual Data Generation Method for Reducing Communication in Federated Learning0
Split Learning in 6G Edge Networks0
FLGo: A Fully Customizable Federated Learning Platform0
Timely Asynchronous Hierarchical Federated Learning: Age of Convergence0
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central UpdatesCode0
Personalized Federated Learning with Feature Alignment and Classifier CollaborationCode1
Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and ImplementationCode0
Randomized Quantization is All You Need for Differential Privacy in Federated LearningCode0
FedNoisy: Federated Noisy Label Learning BenchmarkCode1
Practical and General Backdoor Attacks against Vertical Federated Learning0
Differentially Private Over-the-Air Federated Learning Over MIMO Fading Channels0
Adaptive Federated Learning with Auto-Tuned ClientsCode0
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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