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 15011550 of 6771 papers

TitleStatusHype
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation AnalysisCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective OptimizationCode0
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual ClassificationCode0
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated LearningCode0
Communication-Efficient Design of Learning System for Energy Demand Forecasting of Electrical VehiclesCode0
Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level OptimizationCode0
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMCode0
FedCert: Federated Accuracy CertificationCode0
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated LearningCode0
InFL-UX: A Toolkit for Web-Based Interactive Federated LearningCode0
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray DataCode0
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated LearningCode0
Instance-wise Batch Label Restoration via Gradients in Federated LearningCode0
FedCCRL: Federated Domain Generalization with Cross-Client Representation LearningCode0
Federated Causal Discovery From InterventionsCode0
A Federated Approach to Predicting Emojis in Hindi TweetsCode0
FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated LearningCode0
FedCore: Straggler-Free Federated Learning with Distributed CoresetsCode0
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless NetworksCode0
Communication-Efficient and Privacy-Adaptable Mechanism for Federated LearningCode0
IPLS : A Framework for Decentralized Federated LearningCode0
Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution StrategiesCode0
Is Non-IID Data a Threat in Federated Online Learning to Rank?Code0
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID DataCode0
Just a Simple Transformation is Enough for Data Protection in Vertical Federated LearningCode0
FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated LearningCode0
Knowledge-Injected Federated LearningCode0
Krum Federated Chain (KFC): Using blockchain to defend against adversarial attacks in Federated LearningCode0
FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated LearningCode0
Communication-Efficient ADMM-based Federated LearningCode0
Label Leakage in Federated Inertial-based Human Activity RecognitionCode0
FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID DataCode0
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal TransportCode0
FedAPM: Federated Learning via ADMM with Partial Model PersonalizationCode0
FedAH: Aggregated Head for Personalized Federated LearningCode0
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved RatesCode0
Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated LearningCode0
FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures OptimizerCode0
Learning to Backdoor Federated LearningCode0
FedCAP: Robust Federated Learning via Customized Aggregation and PersonalizationCode0
FCA: Taming Long-tailed Federated Medical Image Classification by Classifier AnchoringCode0
FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated LearningCode0
URVFL: Undetectable Data Reconstruction Attack on Vertical Federated LearningCode0
LightTR: A Lightweight Framework for Federated Trajectory RecoveryCode0
FDAPT: Federated Domain-adaptive Pre-training for Language ModelsCode0
A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent EnterprisesCode0
LLM-QFL: Distilling Large Language Model for Quantum Federated LearningCode0
Fairness and Privacy in Federated Learning and Their Implications in HealthcareCode0
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome PredictionCode0
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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