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 66016650 of 6771 papers

TitleStatusHype
A Crowdsourcing Framework for On-Device Federated Learning0
Device Scheduling with Fast Convergence for Wireless Federated Learning0
Federated Learning with Differential Privacy: Algorithms and Performance Analysis0
Robust Federated Learning with Noisy Communication0
Energy-Aware Analog Aggregation for Federated Learning with Redundant Data0
On the Convergence of Local Descent Methods in Federated Learning0
Federated Learning over Wireless Networks: Convergence Analysis and Resource AllocationCode1
Distributed Networked Learning with Correlated Data0
Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data0
Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning0
Bandwidth Slicing to Boost Federated Learning in Edge Computing0
Gradient Sparification for Asynchronous Distributed Training0
Stochastic Channel-Based Federated Learning for Medical Data Privacy Preserving0
Abnormal Client Behavior Detection in Federated Learning0
Meta Matrix Factorization for Federated Rating Predictions0
Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach0
Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge IntelligenceCode1
Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data0
Federated Generative Privacy0
Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating0
Overcoming Forgetting in Federated Learning on Non-IID Data0
Real-World Image Datasets for Federated LearningCode0
SCAFFOLD: Stochastic Controlled Averaging for Federated LearningCode1
Federated Learning for Coalition Operations0
Eavesdrop the Composition Proportion of Training Labels in Federated Learning0
A blockchain-orchestrated Federated Learning architecture for healthcare consortia0
Central Server Free Federated Learning over Single-sided Trust Social NetworksCode0
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning0
Federated Learning of N-gram Language Models0
Accelerating Federated Learning via Momentum Gradient Descent0
FedMD: Heterogenous Federated Learning via Model DistillationCode1
Differential Privacy-enabled Federated Learning for Sensitive Health Data0
Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence0
Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning0
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy ConstraintsCode0
SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead0
Privacy-preserving Federated Brain Tumour Segmentation0
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization0
Federated User Representation Learning0
Improving Federated Learning Personalization via Model Agnostic Meta LearningCode0
Active Federated Learning0
Federated Learning in Mobile Edge Networks: A Comprehensive Survey0
Model Pruning Enables Efficient Federated Learning on Edge DevicesCode0
On Federated Learning of Deep Networks from Non-IID Data: Parameter Divergence and the Effects of Hyperparametric Methods0
Low Rank Training of Deep Neural Networks for Emerging Memory Technology0
Optimal query complexity for private sequential learning against eavesdropping0
Towards Federated Graph Learning for Collaborative Financial Crimes Detection0
Detailed comparison of communication efficiency of split learning and federated learning0
Measure Contribution of Participants in Federated Learning0
A Joint Learning and Communications Framework for Federated Learning over Wireless NetworksCode0
Show:102550
← PrevPage 133 of 136Next →

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