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 54515500 of 6771 papers

TitleStatusHype
A review of federated learning in renewable energy applications: Potential, challenges, and future directions0
A Review of Privacy-preserving Federated Learning for the Internet-of-Things0
A review on different techniques used to combat the non-IID and heterogeneous nature of data in FL0
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection0
A Robust Federated Learning Framework for Undependable Devices at Scale0
Artificial Intelligence-Driven Clinical Decision Support Systems0
Artificial Intelligence Driven UAV-NOMA-MEC in Next Generation Wireless Networks0
Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions0
Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learning0
A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks0
A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks0
A Secure Aggregation for Federated Learning on Long-Tailed Data0
A Secure and Efficient Federated Learning Framework for NLP0
A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications0
A Secure Federated Learning Framework for Residential Short Term Load Forecasting0
A Secure Federated Learning Framework for 5G Networks0
A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique0
A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning0
A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning0
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning0
A Snapshot of the Frontiers of Client Selection in Federated Learning0
Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection0
A State Alignment-Centric Approach to Federated System Identification: The FedAlign Framework0
A Stochastic Gradient Langevin Dynamics Algorithm For Noise Intrinsic Federated Learning0
A Study of Secure Algorithms for Vertical Federated Learning: Take Secure Logistic Regression as an Example0
A study on performance limitations in Federated Learning0
A Survey for Large Language Models in Biomedicine0
Towards Fairness-Aware Federated Learning0
A Survey of Federated Evaluation in Federated Learning0
A Survey of Federated Learning for Connected and Automated Vehicles0
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network0
A Survey of Mobile Computing for the Visually Impaired0
A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments0
A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective0
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy0
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency0
A Survey on Blockchain-Based Federated Learning and Data Privacy0
A Survey on Class Imbalance in Federated Learning0
A Survey on Cluster-based Federated Learning0
A Survey on Decentralized Federated Learning0
A Survey on Efficient Federated Learning Methods for Foundation Model Training0
A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things0
A Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions0
A Survey on Federated Learning in Human Sensing0
A Survey on Federated Recommendation Systems0
A Survey on Heterogeneous Federated Learning0
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks0
A Survey on Participant Selection for Federated Learning in Mobile Networks0
A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security0
A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing0
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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