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 16011650 of 6771 papers

TitleStatusHype
Lightweight Industrial Cohorted Federated Learning for Heterogeneous Assets0
Spiking Neural Networks in Vertical Federated Learning: Performance Trade-offs0
SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices0
A Hybrid Federated Kernel Regularized Least Squares Algorithm0
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes0
Federated Learning for Face Recognition via Intra-subject Self-supervised Learning0
Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory0
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and FairnessCode0
COALA: A Practical and Vision-Centric Federated Learning PlatformCode2
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation0
Resource-Efficient Federated Multimodal Learning via Layer-wise and Progressive Training0
A New Theoretical Perspective on Data Heterogeneity in Federated Optimization0
A Life-long Learning Intrusion Detection System for 6G-Enabled IoV0
Tackling Selfish Clients in Federated LearningCode0
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning0
Poisoning with A Pill: Circumventing Detection in Federated Learning0
Privacy-Preserving Multi-Center Differential Protein Abundance Analysis with FedProt0
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated LearningCode0
Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach0
Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models0
FedPartWhole: Federated domain generalization via consistent part-whole hierarchies0
Universal Medical Imaging Model for Domain Generalization with Data Privacy0
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and ConvergenceCode0
Where is the Testbed for my Federated Learning Research?0
Personalized Multi-tier Federated LearningCode0
Privacy-preserving gradient-based fair federated learning0
Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus AlgorithmCode0
A Framework for testing Federated Learning algorithms using an edge-like environmentCode0
Individualized Federated Learning for Traffic Prediction with Error Driven AggregationCode0
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering0
Jigsaw Game: Federated Clustering0
Proximity-based Self-Federated Learning0
Non-parametric regularization for class imbalance federated medical image classificationCode0
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems0
Comparing Federated Stochastic Gradient Descent and Federated Averaging for Predicting Hospital Length of Stay0
Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience0
Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communication0
CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imaging0
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification0
R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models0
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning0
Harvesting Private Medical Images in Federated Learning Systems with Crafted Models0
Overcoming Catastrophic Forgetting in Federated Class-Incremental Learning via Federated Global Twin Generator0
Combining Federated Learning and Control: A Survey0
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses0
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning0
Novel clustered federated learning based on local lossCode0
Multi-Modal Dataset Creation for Federated Learning with DICOM Structured Reports0
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization0
FedVAE: Trajectory privacy preserving based on Federated Variational AutoEncoder0
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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