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 57015750 of 6771 papers

TitleStatusHype
Privacy-preserving federated prediction of pain intensity change based on multi-center survey data0
Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification0
Privacy-preserving gradient-based fair federated learning0
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning0
Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data0
Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach0
Privacy-Preserving in Blockchain-based Federated Learning Systems0
Privacy-Preserving in Medical Image Analysis: A Review of Methods and Applications0
Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directions0
Privacy-Preserving Learning of Human Activity Predictors in Smart Environments0
Privacy-Preserving Load Forecasting via Personalized Model Obfuscation0
Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy0
Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning0
Privacy-preserving medical image analysis0
Privacy-Preserving Multi-Center Differential Protein Abundance Analysis with FedProt0
Privacy-Preserving Multi-Stage Fall Detection Framework with Semi-supervised Federated Learning and Robotic Vision Confirmation0
Privacy-Preserving Online Content Moderation: A Federated Learning Use Case0
Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models0
Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity0
Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection0
Privacy Preserving QoE Modeling using Collaborative Learning0
Privacy-preserving quantum federated learning via gradient hiding0
Privacy-Preserving Self-Taught Federated Learning for Heterogeneous Data0
Privacy-Preserving Sequential Recommendation with Collaborative Confusion0
Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach0
Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning0
Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems0
Privacy-preserving Transfer Learning via Secure Maximum Mean Discrepancy0
Privacy Preserving Vertical Federated Learning for Tree-based Models0
Privacy Protection in Prosumer Energy Management Based on Federated Learning0
Privacy Threats Against Federated Matrix Factorization0
Privacy Threats Analysis to Secure Federated Learning0
Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review0
Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning0
Private Aggregation in Hierarchical Wireless Federated Learning with Partial and Full Collusion0
Private and Communication-Efficient Federated Learning based on Differentially Private Sketches0
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems0
Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions0
Private data sharing between decentralized users through the privGAN architecture0
D2P-Fed: Differentially Private Federated Learning With Efficient Communication0
Private Federated Learning In Real World Application -- A Case Study0
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption0
Private Federated Learning with Autotuned Compression0
Private Federated Learning with Domain Adaptation0
Private Federated Learning with Dynamic Power Control via Non-Coherent Over-the-Air Computation0
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses0
Private Language Model Adaptation for Speech Recognition0
Privately Customizing Prefinetuning to Better Match User Data in Federated Learning0
Private Model Personalization Revisited0
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams0
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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