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 1101–1150 of 6771 papers

TitleStatusHype
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients—0
Safe-EF: Error Feedback for Nonsmooth Constrained OptimizationCode0
DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information—0
User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data—0
FedRE: Robust and Effective Federated Learning with Privacy Preference—0
FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learning—0
Federated Learning for Cyber Physical Systems: A Comprehensive Survey—0
Balancing Client Participation in Federated Learning Using AoI—0
Adaptive Biased User Scheduling for Heterogeneous Wireless Federate Learning Network—0
QualBench: Benchmarking Chinese LLMs with Localized Professional Qualifications for Vertical Domain Evaluation—0
Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization—0
FDA-Opt: Communication-Efficient Federated Fine-Tuning of Language ModelsCode0
FedBWO: Enhancing Communication Efficiency in Federated Learning—0
FRAIN to Train: A Fast-and-Reliable Solution for Decentralized Federated LearningCode0
Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning—0
Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments—0
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data—0
Bayesian Robust Aggregation for Federated LearningCode0
Incentivizing Inclusive Contributions in Model Sharing Markets—0
HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models—0
Bayesian Federated Cause-of-Death Classification and Quantification Under Distribution Shift—0
PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework—0
Mitigating Group-Level Fairness Disparities in Federated Visual Language Models—0
Towards Trustworthy Federated Learning with Untrusted Participants—0
Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning—0
Federated Adapter on Foundation Models: An Out-Of-Distribution Approach—0
Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks—0
Mitigating Adversarial Attacks on ECG Classification in Federated Learning via Adversarial TrainingCode0
AI-Driven IRM: Transforming insider risk management with adaptive scoring and LLM-based threat detection—0
Open-Source LLM-Driven Federated Transformer for Predictive IoV Management—0
FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving—0
Communication-Efficient Wireless Federated Fine-Tuning for Large-Scale AI Models—0
Sparsification Under Siege: Defending Against Poisoning Attacks in Communication-Efficient Federated Learning—0
Whispers of Data: Unveiling Label Distributions in Federated Learning Through Virtual Client Simulation—0
A Generalized Meta Federated Learning Framework with Theoretical Convergence Guarantees—0
Online Federation For Mixtures of Proprietary Agents with Black-Box Encoders—0
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs—0
Hubs and Spokes Learning: Efficient and Scalable Collaborative Machine Learning—0
Learning and Generalization with Mixture Data—0
AI-Based Crypto Tokens: The Illusion of Decentralized AI?—0
Federated One-Shot Learning with Data Privacy and Objective-Hiding—0
Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions—0
Clustering-Based Evolutionary Federated Multiobjective Optimization and Learning—0
Federated learning, ethics, and the double black box problem in medical AI—0
Robust Federated Personalised Mean Estimation for the Gaussian Mixture Model—0
Soft-Label Caching and Sharpening for Communication-Efficient Federated DistillationCode0
A Unified Benchmark of Federated Learning with Kolmogorov-Arnold Networks for Medical ImagingCode0
Financial Data Analysis with Robust Federated Logistic RegressionCode0
FedAvgen: Metadata for Model Aggregation In Communication Systems—0
Harmonizing Generalization and Personalization in Ring-topology Decentralized Federated Learning—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