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 57515800 of 6771 papers

TitleStatusHype
A Novel Attribute Reconstruction Attack in Federated Learning0
Federated Asymptotics: a model to compare federated learning algorithms0
Blockchain-based Trustworthy Federated Learning Architecture0
Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification0
Collaborative Unsupervised Visual Representation Learning from Decentralized DataCode0
A Contract Theory based Incentive Mechanism for Federated Learning0
Dynamic Attention-based Communication-Efficient Federated Learning0
An Operator Splitting View of Federated Learning0
Communication Optimization in Large Scale Federated Learning using Autoencoder Compressed Weight Updates0
ABC-FL: Anomalous and Benign client Classification in Federated Learning0
FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning0
FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning0
The Effect of Training Parameters and Mechanisms on Decentralized Federated Learning based on MNIST DatasetCode0
Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption0
User Scheduling for Federated Learning Through Over-the-Air Computation0
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning0
On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator0
Multi-task Federated Edge Learning (MtFEEL) in Wireless Networks0
Fed-BEV: A Federated Learning Framework for Modelling Energy Consumption of Battery Electric Vehicles0
Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application0
Secure and Privacy-Preserving Federated Learning via Co-Utility0
Information Stealing in Federated Learning Systems Based on Generative Adversarial Networks0
Communication-Efficient Federated Learning via Predictive CodingCode0
Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges0
Distributed Learning for Time-varying Networks: A Scalable Design0
Sensing and Mapping for Better Roads: Initial Plan for Using Federated Learning and Implementing a Digital Twin to Identify the Road Conditions in a Developing Country -- Sri Lanka0
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness0
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges0
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning0
HAFLO: GPU-Based Acceleration for Federated Logistic Regression0
New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning0
Federated Learning Meets Natural Language Processing: A Survey0
A Payload Optimization Method for Federated Recommender Systems0
Towards Industrial Private AI: A two-tier framework for data and model security0
Preliminary Steps Towards Federated Sentiment Classification0
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes0
LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning0
Accelerated Gradient Descent Learning over Multiple Access Fading Channels0
Federated Learning with Fair Worker Selection: A Multi-Round Submodular Maximization Approach0
Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things ApplicationCode0
Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning0
Communication Efficiency in Federated Learning: Achievements and Challenges0
Federated Learning Versus Classical Machine Learning: A Convergence Comparison0
Fed-ensemble: Improving Generalization through Model Ensembling in Federated LearningCode0
Defending against Reconstruction Attack in Vertical Federated Learning0
How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?0
Precision-Weighted Federated Learning0
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions0
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression0
Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach0
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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