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 301350 of 6771 papers

TitleStatusHype
FDA-Opt: Communication-Efficient Federated Fine-Tuning of Language ModelsCode0
FedBWO: Enhancing Communication Efficiency in Federated Learning0
Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments0
Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning0
Bayesian Robust Aggregation for Federated LearningCode0
Towards One-shot Federated Learning: Advances, Challenges, and Future DirectionsCode4
Incentivizing Inclusive Contributions in Model Sharing Markets0
HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models0
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data0
Bayesian Federated Cause-of-Death Classification and Quantification Under Distribution Shift0
Mitigating Group-Level Fairness Disparities in Federated Visual Language Models0
Towards Trustworthy Federated Learning with Untrusted Participants0
Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning0
PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework0
Federated Adapter on Foundation Models: An Out-Of-Distribution Approach0
Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks0
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 detection0
Communication-Efficient Wireless Federated Fine-Tuning for Large-Scale AI Models0
FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving0
Open-Source LLM-Driven Federated Transformer for Predictive IoV Management0
A Generalized Meta Federated Learning Framework with Theoretical Convergence Guarantees0
Online Federation For Mixtures of Proprietary Agents with Black-Box Encoders0
Sparsification Under Siege: Defending Against Poisoning Attacks in Communication-Efficient Federated Learning0
Whispers of Data: Unveiling Label Distributions in Federated Learning Through Virtual Client Simulation0
AI-Based Crypto Tokens: The Illusion of Decentralized AI?0
Clustering-Based Evolutionary Federated Multiobjective Optimization and Learning0
Learning and Generalization with Mixture Data0
Federated learning, ethics, and the double black box problem in medical AI0
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs0
Hubs and Spokes Learning: Efficient and Scalable Collaborative Machine Learning0
Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions0
Federated One-Shot Learning with Data Privacy and Objective-Hiding0
FedAvgen: Metadata for Model Aggregation In Communication Systems0
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
Robust Federated Personalised Mean Estimation for the Gaussian Mixture Model0
Harmonizing Generalization and Personalization in Ring-topology Decentralized Federated Learning0
Federated Learning-based Semantic Segmentation for Lane and Object Detection in Autonomous Driving0
UnifyFL: Enabling Decentralized Cross-Silo Federated LearningCode0
Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity0
Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction0
Switch-Based Multi-Part Neural Network0
Federated EndoViT: Pretraining Vision Transformers via Federated Learning on Endoscopic Image Collections0
Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach0
Simplified Swarm Learning Framework for Robust and Scalable Diagnostic Services in Cancer Histopathology0
DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models0
Private Federated Learning using Preference-Optimized Synthetic DataCode1
Achieving Distributive Justice in Federated Learning via Uncertainty QuantificationCode0
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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