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 251300 of 6771 papers

TitleStatusHype
Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarios0
Nosy Layers, Noisy Fixes: Tackling DRAs in Federated Learning Systems using Explainable AI0
FedDuA: Doubly Adaptive Federated Learning0
FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations0
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network0
Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning0
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning0
Robust Federated Learning on Edge Devices with Domain Heterogeneity0
Sybil-based Virtual Data Poisoning Attacks in Federated Learning0
Enhancing the Performance of Global Model by Improving the Adaptability of Local Models in Federated Learning0
Energy-Efficient Federated Learning for AIoT using Clustering MethodsCode0
Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data0
Chisme: Fully Decentralized Differentiated Deep Learning for Edge Intelligence0
Toward Malicious Clients Detection in Federated Learning0
Ranking-Based At-Risk Student Prediction Using Federated Learning and Differential FeaturesCode0
Toward Fair Federated Learning under Demographic Disparities and Data ImbalanceCode0
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions0
FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing0
Modular Federated Learning: A Meta-Framework Perspective0
Privacy-Preserving Analytics for Smart Meter (AMI) Data: A Hybrid Approach to Comply with CPUC Privacy Regulations0
Multi-Layer Hierarchical Federated Learning with Quantization0
Sharp Gaussian approximations for Decentralized Federated Learning0
A Federated Random Forest Solution for Secure Distributed Machine LearningCode0
Securing Genomic Data Against Inference Attacks in Federated Learning Environments0
Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices0
FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes0
Adaptive Latent-Space Constraints in Personalized FLCode2
Enhancing Federated Learning with Kolmogorov-Arnold Networks: A Comparative Study Across Diverse Aggregation Strategies0
Personalized Federated Learning under Model Dissimilarity Constraints0
AugMixCloak: A Defense against Membership Inference Attacks via Image Transformation0
MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning0
Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated Intelligence0
Federated Learning with LoRA Optimized DeiT and Multiscale Patch Embedding for Secure Eye Disease Recognition0
Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs0
FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures0
Privacy-aware Berrut Approximated Coded Computing applied to general distributed learning0
FNBench: Benchmarking Robust Federated Learning against Noisy LabelsCode1
RiM: Record, Improve and Maintain Physical Well-being using Federated LearningCode0
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients0
DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information0
Safe-EF: Error Feedback for Nonsmooth Constrained OptimizationCode0
User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data0
FedRE: Robust and Effective Federated Learning with Privacy Preference0
FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning0
Balancing Client Participation in Federated Learning Using AoI0
QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation0
Adaptive Biased User Scheduling for Heterogeneous Wireless Federate Learning Network0
Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization0
Federated Learning for Cyber Physical Systems: A Comprehensive Survey0
FRAIN to Train: A Fast-and-Reliable Solution for Decentralized Federated LearningCode0
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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