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 49014950 of 6771 papers

TitleStatusHype
Efficient Image Representation Learning with Federated Sampled Softmax0
LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data0
The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning0
Differentially Private Federated Learning with Local Regularization and Sparsification0
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning0
Acceleration of Federated Learning with Alleviated Forgetting in Local TrainingCode1
Continual Horizontal Federated Learning for Heterogeneous Data0
Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed DataCode1
Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data0
Label Leakage and Protection from Forward Embedding in Vertical Federated Learning0
Personalized Federated Learning With GraphCode1
FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers0
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks0
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous DrivingCode1
Improving Response Time of Home IoT Services in Federated LearningCode0
Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations0
Graph-Assisted Communication-Efficient Ensemble Federated Learning0
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach0
Sky Computing: Accelerating Geo-distributed Computing in Federated LearningCode1
Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying0
Partitioned Variational Inference: A Framework for Probabilistic Federated LearningCode0
FedCAT: Towards Accurate Federated Learning via Device Concatenation0
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization0
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search0
TEE-based decentralized recommender systems: The raw data sharing redemptionCode0
Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation0
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection0
Privacy Leakage of Adversarial Training Models in Federated Learning SystemsCode1
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
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy0
Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning0
Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates0
FedEmbed: Personalized Private Federated Learning0
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks0
ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!0
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing0
FLAME: Federated Learning Across Multi-device Environments0
Cross-Silo Heterogeneous Model Federated Multitask LearningCode0
LAMP: Extracting Text from Gradients with Language Model PriorsCode1
Federated Stochastic Gradient Descent Begets Self-Induced Momentum0
Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning0
An Equivalence Between Data Poisoning and Byzantine Gradient AttacksCode0
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model0
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis0
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams0
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices0
Federated-Learning-Based Anomaly Detection for IoT Security Attacks0
Towards Verifiable 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