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 40514100 of 6771 papers

TitleStatusHype
DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private DecoderCode0
FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative TrainingCode1
Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization0
Learning to Generate Image Embeddings with User-level Differential Privacy0
Scalable Collaborative Learning via Representation Sharing0
DYNAFED: Tackling Client Data Heterogeneity with Global DynamicsCode1
Non-Coherent Over-the-Air Decentralized Gradient Descent0
Personalized Federated Learning with Hidden Information on Personalized Prior0
FedSiam-DA: Dual-aggregated Federated Learning via Siamese Network under Non-IID Data0
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous DataCode1
Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery0
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine0
Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality0
Quantifying the Impact of Label Noise on Federated Learning0
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and ChallengesCode1
Personalized Federated Learning with Multi-branch Architecture0
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative LearningCode1
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers0
Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges0
Bayesian Federated Neural Matching that Completes Full Information0
Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing0
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning0
FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User HeterogeneityCode1
Universal EHR Federated Learning FrameworkCode1
Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things0
Adaptive Federated Minimax Optimization with Lower Complexities0
Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring0
FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model0
Differentially Private Vertical Federated Learning0
Towards Privacy-Aware Causal Structure Learning in Federated SettingCode0
Quantum Split Neural Network Learning using Cross-Channel Pooling0
Federated Unsupervised Visual Representation Learning via Exploiting General Content and Personal Style0
A Federated Approach to Predicting Emojis in Hindi TweetsCode0
From Competition to Collaboration: Making Toy Datasets on Kaggle Clinically Useful for Chest X-Ray Diagnosis Using Federated Learning0
FedLesScan: Mitigating Stragglers in Serverless Federated LearningCode1
Secure Aggregation Is Not All You Need: Mitigating Privacy Attacks with Noise Tolerance in Federated Learning0
Robust Smart Home Face Recognition under Starving Federated DataCode0
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization0
Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication0
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search0
Almost Tight Error Bounds on Differentially Private Continual Counting0
Knowledge Distillation for Federated Learning: a Practical Guide0
Framework Construction of an Adversarial Federated Transfer Learning Classifier0
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism DesignCode1
Clustered Federated Learning based on Nonconvex Pairwise FusionCode0
Enhancing Efficiency in Multidevice Federated Learning through Data SelectionCode1
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design0
A Penalty-Based Method for Communication-Efficient Decentralized Bilevel Programming0
Federated Learning Using Three-Operator ADMM0
FedGrad: Optimisation in Decentralised Machine Learning0
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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