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 28512900 of 6771 papers

TitleStatusHype
Empowering Data Mesh with Federated LearningCode0
Secure Aggregation is Not Private Against Membership Inference AttacksCode0
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning0
GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning0
Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study0
FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN0
Distributed collaborative anomalous sound detection by embedding sharing0
Differentially Private Online Federated Learning with Correlated Noise0
Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients0
Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data0
FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data0
FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning0
Heterogeneous Federated Learning with Splited Language Model0
A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures0
TablePuppet: A Generic Framework for Relational Federated LearningCode0
Initialisation and Network Effects in Decentralised Federated Learning0
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization0
Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation0
Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models0
FedMef: Towards Memory-efficient Federated Dynamic Pruning0
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central ServerCode0
Advancing IIoT with Over-the-Air Federated Learning: The Role of Iterative Magnitude Pruning0
Byzantine-resilient Federated Learning With Adaptivity to Data Heterogeneity0
Leveraging feature communication in federated learning for remote sensing image classification0
When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse WeatherCode0
Fed-RAC: Resource-Aware Clustering for Tackling Heterogeneity of Participants in Federated LearningCode0
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis0
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing0
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks0
FedSR: A Semi-Decentralized Federated Learning Algorithm for Non-IIDness in IoT System0
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency0
Improving LoRA in Privacy-preserving Federated Learning0
FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter UpdateCode0
KnFu: Effective Knowledge Fusion0
Federated Transfer Learning with Differential Privacy0
Enhancing IoT Security Against DDoS Attacks through Federated Learning0
FedQNN: Federated Learning using Quantum Neural Networks0
Federated Learning based on Pruning and RecoveryCode0
Feature Aggregation with Latent Generative Replay for Federated Continual Learning of Socially Appropriate Robot Behaviours0
FAGH: Accelerating Federated Learning with Approximated Global Hessian0
Empowering Healthcare through Privacy-Preserving MRI Analysis0
Learning from straggler clients in federated learning0
Defense via Behavior Attestation against Attacks in Connected and Automated Vehicles based Federated Learning Systems0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning0
Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios0
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models0
Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains0
Fairness-Aware Multi-Server Federated Learning Task Delegation over Wireless Networks0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems0
AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in Federated 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