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 52015250 of 6771 papers

TitleStatusHype
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error AnalysisCode0
Interpretability of Fine-grained Classification of Sadness and DepressionCode0
Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs0
Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm0
Federated Learning Approach for Lifetime Prediction of Semiconductor Lasers0
Fair Federated Learning via Bounded Group Loss0
Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing0
Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey0
Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation0
MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients0
Training a Tokenizer for Free with Private Federated Learning0
Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases0
Privatized Graph Federated Learning0
The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining0
Communication-Efficient Federated Distillation with Active Data Sampling0
Private Non-Convex Federated Learning Without a Trusted ServerCode0
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation0
Federated Remote Physiological Measurement with Imperfect Data0
Wireless Quantized Federated Learning: A Joint Computation and Communication Design0
No Free Lunch Theorem for Security and Utility in Federated Learning0
FedSyn: Synthetic Data Generation using Federated Learning0
A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles0
A Contribution-based Device Selection Scheme in Federated Learning0
Update Compression for Deep Neural Networks on the Edge0
Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition0
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms0
Efficient Image Representation Learning with Federated Sampled Softmax0
LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data0
Differentially Private Federated Learning with Local Regularization and Sparsification0
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning0
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning0
Continual Horizontal Federated Learning for Heterogeneous Data0
Label Leakage and Protection from Forward Embedding in Vertical Federated Learning0
Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data0
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks0
FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers0
Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations0
Improving Response Time of Home IoT Services in Federated LearningCode0
Graph-Assisted Communication-Efficient Ensemble Federated Learning0
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach0
Partitioned Variational Inference: A Framework for Probabilistic Federated LearningCode0
Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying0
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization0
TEE-based decentralized recommender systems: The raw data sharing redemptionCode0
FedCAT: Towards Accurate Federated Learning via Device Concatenation0
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search0
Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation0
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection0
BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search enginesCode0
Personalized Federated Learning with Exact Stochastic Gradient Descent0
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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