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 35513600 of 6771 papers

TitleStatusHype
Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics0
SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems0
Scheduling Algorithms for Federated Learning with Minimal Energy Consumption0
Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks0
Scheduling and Communication Schemes for Decentralized Federated Learning0
Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellations0
Scheduling for On-Board Federated Learning with Satellite Clusters0
Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC0
ScionFL: Efficient and Robust Secure Quantized Aggregation0
SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training0
Seamless Integration: Sampling Strategies in Federated Learning Systems0
SecFL: Confidential Federated Learning using TEEs0
Second-Order Guarantees in Federated Learning0
Secure Aggregation for Buffered Asynchronous Federated Learning0
Secure Aggregation for Federated Learning in Flower0
Secure Aggregation Is Not All You Need: Mitigating Privacy Attacks with Noise Tolerance in Federated Learning0
Secure and Efficient Federated Learning Through Layering and Sharding Blockchain0
Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation0
Secure and Fast Asynchronous Vertical Federated Learning via Cascaded Hybrid Optimization0
Secure and Privacy-Preserving Federated Learning via Co-Utility0
Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation0
Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge Proofs0
SECure: A Social and Environmental Certificate for AI Systems0
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating0
SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree0
SecureBoost: A Lossless Federated Learning Framework0
SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning0
Secure Byzantine-Robust Distributed Learning via Clustering0
Secure Byzantine-Robust Federated Learning with Dimension-free Error0
Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks0
SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning0
Secure Decentralized Learning with Blockchain0
Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System0
Secure Federated Clustering0
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 20
Secure Federated Learning Approaches to Diagnosing COVID-190
Secure Federated Learning for Cognitive Radio Sensing0
Privacy-preserving Federated Learning for Residential Short Term Load Forecasting0
Secure Federated Learning in 5G Mobile Networks0
Secure Federated Learning of User Verification Models0
Secure Federated Matrix Factorization0
SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning0
Secure Forward Aggregation for Vertical Federated Neural Networks0
Secure Generalization through Stochastic Bidirectional Parameter Updates Using Dual-Gradient Mechanism0
Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection0
Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systems0
Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption0
Secure short-term load forecasting for smart grids with transformer-based federated learning0
Secure Sum Outperforms Homomorphic Encryption in (Current) Collaborative Deep Learning0
Secure Visual Data Processing via 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