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 42014250 of 6771 papers

TitleStatusHype
Robust Federated Learning for execution time-based device model identification under label-flipping attack0
Robust Federated Learning for Neural Networks0
Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation0
Robust Federated Learning in a Heterogeneous Environment0
Robust Federated Learning in the Face of Covariate Shift: A Magnitude Pruning with Hybrid Regularization Framework for Enhanced Model Aggregation0
Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach0
Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks0
Robust Federated Learning on Edge Devices with Domain Heterogeneity0
Robust Federated Learning Over the Air: Combating Heavy-Tailed Noise with Median Anchored Clipping0
Robust Federated Learning: The Case of Affine Distribution Shifts0
Robust Federated Learning Through Representation Matching and Adaptive Hyper-parameters0
Robust Federated Learning via Over-The-Air Computation0
Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data0
Robust Federated Learning with Connectivity Failures: A Semi-Decentralized Framework with Collaborative Relaying0
Robust Federated Learning with Global Sensitivity Estimation for Financial Risk Management0
Robust Federated Learning with Noisy Communication0
Robust Federated Personalised Mean Estimation for the Gaussian Mixture Model0
Robust Federated Training via Collaborative Machine Teaching using Trusted Instances0
Robust Knowledge Adaptation for Federated Unsupervised Person ReID0
Optimal Robust Learning of Discrete Distributions from Batches0
Robust Learning Protocol for Federated Tumor Segmentation Challenge0
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations0
Robust Networked Federated Learning for Localization0
Robust Over-the-Air Computation with Type-Based Multiple Access0
Robust Quantity-Aware Aggregation for Federated Learning0
Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones0
Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework0
RobWE: Robust Watermark Embedding for Personalized Federated Learning Model Ownership Protection0
RPN: A Residual Pooling Network for Efficient Federated Learning0
R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models0
RVFR: Robust Vertical Federated Learning via Feature Subspace Recovery0
SAB:A Stealing and Robust Backdoor Attack based on Steganographic Algorithm against Federated Learning0
SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspection0
SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead0
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications0
SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning0
Safeguarding Federated Learning-based Road Condition Classification0
SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation0
SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization0
Efficient Sparse Secure Aggregation for Federated Learning0
SAFE: Self-Adjustment Federated Learning Framework for Remote Sensing Collaborative Perception0
SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)0
SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning0
SaFL: Sybil-aware Federated Learning with Application to Face Recognition0
SAGDA: Achieving O(ε^-2) Communication Complexity in Federated Min-Max Learning0
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries0
SalientGrads: Sparse Models for Communication Efficient and Data Aware Distributed Federated Training0
Samplable Anonymous Aggregation for Private Federated Data Analysis0
Sample-based Federated Learning via Mini-batch SSCA0
SAPAG: A Self-Adaptive Privacy Attack From Gradients0
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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