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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1051–1100 of 6771 papers

TitleStatusHype
Traceable Black-box Watermarks for Federated Learning—0
Cross-Cloud Data Privacy Protection: Optimizing Collaborative Mechanisms of AI Systems by Integrating Federated Learning and LLMs—0
A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis—0
γ-FedHT: Stepsize-Aware Hard-Threshold Gradient Compression in Federated Learning—0
FedHQ: Hybrid Runtime Quantization for Federated Learning—0
Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners—0
FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious ClientsCode0
Random Client Selection on Contrastive Federated Learning for Tabular Data—0
Federated Low-Rank Adaptation for Foundation Models: A SurveyCode0
Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning—0
Quantum-Evolutionary Neural Networks for Multi-Agent Federated Learning—0
Joint Graph Estimation and Signal Restoration for Robust Federated Learning—0
Multi-Modal Multi-Task (M3T) Federated Foundation Models for Embodied AI: Potentials and Challenges for Edge Integration—0
Heterogeneity-Aware Client Sampling: A Unified Solution for Consistent Federated Learning—0
FedDuA: Doubly Adaptive Federated Learning—0
Deep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarios—0
Nosy Layers, Noisy Fixes: Tackling DRAs in Federated Learning Systems using Explainable AI—0
FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations—0
Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning—0
Enhancing the Performance of Global Model by Improving the Adaptability of Local Models in Federated Learning—0
Robust Federated Learning on Edge Devices with Domain Heterogeneity—0
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network—0
Sybil-based Virtual Data Poisoning Attacks in Federated Learning—0
Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning—0
Chisme: Fully Decentralized Differentiated Deep Learning for Edge Intelligence—0
Energy-Efficient Federated Learning for AIoT using Clustering MethodsCode0
Ranking-Based At-Risk Student Prediction Using Federated Learning and Differential FeaturesCode0
Toward Fair Federated Learning under Demographic Disparities and Data ImbalanceCode0
Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data—0
Toward Malicious Clients Detection in Federated Learning—0
FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing—0
Multi-Layer Hierarchical Federated Learning with Quantization—0
Privacy-Preserving Analytics for Smart Meter (AMI) Data: A Hybrid Approach to Comply with CPUC Privacy Regulations—0
Modular Federated Learning: A Meta-Framework Perspective—0
Federated Large Language Models: Feasibility, Robustness, Security and Future Directions—0
Sharp Gaussian approximations for Decentralized Federated Learning—0
Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices—0
Personalized Federated Learning under Model Dissimilarity Constraints—0
FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes—0
Enhancing Federated Learning with Kolmogorov-Arnold Networks: A Comparative Study Across Diverse Aggregation Strategies—0
A Federated Random Forest Solution for Secure Distributed Machine LearningCode0
Securing Genomic Data Against Inference Attacks in Federated Learning Environments—0
Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs—0
AugMixCloak: A Defense against Membership Inference Attacks via Image Transformation—0
Federated Learning with LoRA Optimized DeiT and Multiscale Patch Embedding for Secure Eye Disease Recognition—0
MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning—0
Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated Intelligence—0
Privacy-aware Berrut Approximated Coded Computing applied to general distributed learning—0
FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures—0
DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information—0
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All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75—Unverified
2SiloBN + SAMmIoU49.1—Unverified
3SiloBNmIoU45.96—Unverified
4FedSAM + SWAmIoU43.42—Unverified
5FedASAM + SWAmIoU43.02—Unverified
6FedAvg + SWAmIoU42.48—Unverified
7FedASAMmIoU42.27—Unverified
8FedSAMmIoU41.22—Unverified
9FedAvgmIoU38.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32—Unverified
2FedSAM + SWAAcc@1-1262Clients68.12—Unverified
3FedAvg + SWAAcc@1-1262Clients67.52—Unverified
4FedASAMAcc@1-1262Clients64.23—Unverified
5FedSAMAcc@1-1262Clients63.72—Unverified
6FedAvgAcc@1-1262Clients61.91—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64—Unverified
2FedASAMACC@1-100Clients39.76—Unverified
3FedSAM + SWAACC@1-100Clients39.51—Unverified
4FedSAMACC@1-100Clients36.93—Unverified
5FedAvgACC@1-100Clients36.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62—Unverified
2FedASAMACC@1-100Clients40.81—Unverified
3FedSAM + SWAACC@1-100Clients39.24—Unverified
4FedAvgACC@1-100Clients38.59—Unverified
5FedSAMACC@1-100Clients38.56—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72—Unverified
2FedSAM + SWAACC@1-100Clients46.76—Unverified
3FedASAMACC@1-100Clients46.58—Unverified
4FedSAMACC@1-100Clients44.84—Unverified
5FedAvgACC@1-100Clients41.27—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27—Unverified
2FedASAMACC@1-100Clients47.78—Unverified
3FedSAM + SWAACC@1-100Clients46.47—Unverified
4FedSAMACC@1-100Clients46.05—Unverified
5FedAvgACC@1-100Clients42.17—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17—Unverified
2FedSAM + SWAACC@1-100Clients47.96—Unverified
3FedASAMACC@1-100Clients45.61—Unverified
4FedSAMACC@1-100Clients44.73—Unverified
5FedAvgACC@1-100Clients40.43—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01—Unverified
2FedSAM + SWAACC@1-100Clients39.3—Unverified
3FedASAMACC@1-100Clients36.04—Unverified
4FedSAMACC@1-100Clients31.04—Unverified
5FedAvgACC@1-100Clients30.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97—Unverified
2FedASAM + SWAACC@1-100Clients54.79—Unverified
3FedSAM + SWAACC@1-100Clients53.67—Unverified
4FedSAMACC@1-100Clients53.39—Unverified
5FedAvgACC@1-100Clients50.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5—Unverified
2FedSAM + SWAACC@1-100Clients54.36—Unverified
3FedASAM + SWAACC@1-100Clients54.1—Unverified
4FedSAMACC@1-100Clients53.97—Unverified
5FedAvgACC@1-100Clients50.66—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81—Unverified
2FedSAMACC@1-100Clients54.01—Unverified
3FedSAM + SWAACC@1-100Clients53.9—Unverified
4FedASAM + SWAACC@1-100Clients53.86—Unverified
5FedAvgACC@1-100Clients49.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2—Unverified