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 38513900 of 6771 papers

TitleStatusHype
Predictive Maintenance for Optical Networks in Robust Collaborative Learning0
Preliminary Steps Towards Federated Sentiment Classification0
Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality0
Preserving Privacy and Security in Federated Learning0
Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation0
Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification0
Pre-trained Model Guided Mixture Knowledge Distillation for Adversarial Federated Learning0
Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs0
Price-Discrimination Game for Distributed Resource Management in Federated Learning0
Price of Stability in Quality-Aware Federated Learning0
PrIeD-KIE: Towards Privacy Preserved Document Key Information Extraction0
Principles and Components of Federated Learning Architectures0
Prior-Independent Auctions for the Demand Side of Federated Learning0
Prioritized Multi-Criteria Federated Learning0
Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning0
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning0
PriPrune: Quantifying and Preserving Privacy in Pruned Federated Learning0
Privacy Against Agnostic Inference Attacks in Vertical Federated Learning0
Privacy Against Inference Attacks in Vertical Federated Learning0
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation0
Privacy Amplification via Random Participation in Federated Learning0
Privacy Amplification via Random Check-Ins0
Privacy and Fairness in Federated Learning: on the Perspective of Trade-off0
Privacy and Robustness in Federated Learning: Attacks and Defenses0
Privacy Assessment of Federated Learning using Private Personalized Layers0
Privacy Attack in Federated Learning is Not Easy: An Experimental Study0
Privacy attacks for automatic speech recognition acoustic models in a federated learning framework0
Privacy-aware Berrut Approximated Coded Computing for Federated Learning0
Privacy-aware Berrut Approximated Coded Computing applied to general distributed learning0
MeanCache: User-Centric Semantic Caching for LLM Web Services0
Privacy-Aware Spectrum Pricing and Power Control Optimization for LEO Satellite Internet-of-Things0
Privacy Drift: Evolving Privacy Concerns in Incremental Learning0
Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning0
Privacy-Enhanced Over-the-Air Federated Learning via Client-Driven Power Balancing0
Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry0
Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios0
Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions0
Privacy in Multimodal Federated Human Activity Recognition0
Privacy is All You Need: Revolutionizing Wearable Health Data with Advanced PETs0
Privacy is What We Care About: Experimental Investigation of Federated Learning on Edge Devices0
Privacy of federated QR decomposition using additive secure multiparty computation0
Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey0
Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspective0
Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans0
Privacy-Preserved Taxi Demand Prediction System Utilizing Distributed Data0
Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustness0
Privacy-Preserving Analytics for Smart Meter (AMI) Data: A Hybrid Approach to Comply with CPUC Privacy Regulations0
Privacy Preserving and Robust Aggregation for Cross-Silo Federated Learning in Non-IID Settings0
Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles0
Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative 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