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 47514800 of 6771 papers

TitleStatusHype
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling0
Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data0
AGIC: Approximate Gradient Inversion Attack on Federated Learning0
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated FeaturesCode1
Personalized Federated Learning with Multiple Known ClustersCode0
Decision Models for Selecting Federated Learning Architecture Patterns0
Improving the Robustness of Federated Learning for Severely Imbalanced Datasets0
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias EstimationCode0
FedShuffle: Recipes for Better Use of Local Work in Federated Learning0
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning0
Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification0
Poisoning Deep Learning Based Recommender Model in Federated Learning ScenariosCode1
A review of Federated Learning in Intrusion Detection Systems for IoT0
Time-triggered Federated Learning over Wireless Networks0
Federated Stochastic Primal-dual Learning with Differential Privacy0
One-shot Federated Learning without Server-side TrainingCode0
Enhancing Privacy against Inversion Attacks in Federated Learning by using Mixing Gradients Strategies0
PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communications0
Analysing the Influence of Attack Configurations on the Reconstruction of Medical Images in Federated Learning0
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server0
Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System0
Federated Learning with Lossy Distributed Source Coding: Analysis and Optimization0
GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV0
Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets0
Federated Contrastive Learning for Volumetric Medical Image Segmentation0
Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy0
A Closer Look at Personalization in Federated Image Classification0
Federated Learning Enables Big Data for Rare Cancer Boundary DetectionCode1
Federated Learning via Inexact ADMMCode1
Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Model0
Efficient Wireless Federated Learning with Partial Model Aggregation0
Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database0
Is Non-IID Data a Threat in Federated Online Learning to Rank?Code0
Federated Learning for Distributed Energy-Efficient Resource Allocation0
A Practical Cross-Device Federated Learning Framework over 5G Networks0
PrivateRec: Differentially Private Training and Serving for Federated News Recommendation0
FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL DivergenceCode1
Federated Learning Cost Disparity for IoT Devices0
Self-Aware Personalized Federated Learning0
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup0
Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case0
FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning0
IOP-FL: Inside-Outside Personalization for Federated Medical Image SegmentationCode1
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning0
A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations0
Learning Task-Aware Energy Disaggregation: a Federated ApproachCode0
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated DistillationCode1
HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT NetworksCode0
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning0
Distributed learning optimisation of Cox models can leak patient data: Risks and solutions0
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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