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 801850 of 6771 papers

TitleStatusHype
SCAFFOLD: Stochastic Controlled Averaging for Federated LearningCode1
FedMD: Heterogenous Federated Learning via Model DistillationCode1
Measuring the Effects of Non-Identical Data Distribution for Federated Visual ClassificationCode1
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance ReductionCode1
Decentralized Federated Learning: A Segmented Gossip ApproachCode1
On Analog Gradient Descent Learning over Multiple Access Fading ChannelsCode1
On the Convergence of FedAvg on Non-IID DataCode1
Interpret Federated Learning with Shapley ValuesCode1
Differentially Private Learning with Adaptive ClippingCode1
Towards Federated Learning at Scale: System DesignCode1
Agnostic Federated LearningCode1
Federated Optimization in Heterogeneous NetworksCode1
Applied Federated Learning: Improving Google Keyboard Query SuggestionsCode1
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated LearningCode1
LEAF: A Benchmark for Federated SettingsCode1
Analyzing Federated Learning through an Adversarial LensCode1
How To Backdoor Federated LearningCode1
Differentially Private Federated Learning: A Client Level PerspectiveCode1
Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient DescentCode1
Communication-Efficient Learning of Deep Networks from Decentralized DataCode1
A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraintsCode0
Federated Learning for Commercial Image Sources0
FedGA: A Fair Federated Learning Framework Based on the Gini Coefficient0
Safeguarding Federated Learning-based Road Condition Classification0
Site-Level Fine-Tuning with Progressive Layer Freezing: Towards Robust Prediction of Bronchopulmonary Dysplasia from Day-1 Chest Radiographs in Extremely Preterm Infants0
Federated Learning in Open- and Closed-Loop EMG Decoding: A Privacy and Performance Perspective0
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning0
Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks0
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy0
Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise0
FLsim: A Modular and Library-Agnostic Simulation Framework for Federated LearningCode0
D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data0
Quantized Rank Reduction: A Communications-Efficient Federated Learning Scheme for Network-Critical Applications0
ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs0
Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout0
Domain Borders Are There to Be Crossed With Federated Few-Shot AdaptationCode0
Privacy-Preserving Multi-Stage Fall Detection Framework with Semi-supervised Federated Learning and Robotic Vision Confirmation0
MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping0
Convergence of Agnostic Federated Averaging0
Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning0
DRAGD: A Federated Unlearning Data Reconstruction Attack Based on Gradient Differences0
Federated Learning with Graph-Based Aggregation for Traffic Forecasting0
Lightweight Federated Learning over Wireless Edge Networks0
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise0
SFedKD: Sequential Federated Learning with Discrepancy-Aware Multi-Teacher Knowledge Distillation0
Model Parallelism With Subnetwork Data Parallelism0
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift0
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry DetectionCode0
HLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger Fabric0
Sparse Self-Federated Learning for Energy Efficient Cooperative Intelligence in Society 5.00
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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