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 56515700 of 6771 papers

TitleStatusHype
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
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings0
Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing0
Privacy Preserving Charge Location Prediction for Electric Vehicles0
Privacy-Preserving Chest X-ray Report Generation via Multimodal Federated Learning with ViT and GPT-20
Collaborative Chinese Text Recognition with Personalized Federated Learning0
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment0
Privacy-Preserving Cooperative Visible Light Positioning for Nonstationary Environment: A Federated Learning Perspective0
Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions0
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation0
Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach0
Privacy-preserving Decentralized Aggregation for Federated Learning0
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph0
Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters0
Privacy-preserving design of graph neural networks with applications to vertical federated learning0
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation0
FedPower: Privacy-Preserving Distributed Eigenspace Estimation0
Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability0
Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data0
Privacy-preserving Federated Brain Tumour Segmentation0
Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation0
Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence0
Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption0
Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management0
Privacy-Preserving Federated Learning on Partitioned Attributes0
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection0
Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryption0
Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks0
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks0
Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator0
Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing0
Show:102550
← PrevPage 114 of 136Next →

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