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 24012450 of 6771 papers

TitleStatusHype
FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning0
FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation0
FedEmbed: Personalized Private Federated Learning0
Communication-Efficient Wireless Federated Fine-Tuning for Large-Scale AI Models0
A Survey for Large Language Models in Biomedicine0
FedEPA: Enhancing Personalization and Modality Alignment in Multimodal Federated Learning0
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation0
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition0
Federal Learning Framework for Quality Evaluation of Blastomere Cleavage0
FedeRank: User Controlled Feedback with Federated Recommender Systems0
Federated Action Recognition on Heterogeneous Embedded Devices0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning0
Communication-robust and Privacy-safe Distributed Estimation for Heterogeneous Community-level Behind-the-meter Solar Power Generation0
Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification0
FedDiscrete: A Secure Federated Learning Algorithm Against Weight Poisoning0
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning0
Federated Adapter on Foundation Models: An Out-Of-Distribution Approach0
Federated EndoViT: Pretraining Vision Transformers via Federated Learning on Endoscopic Image Collections0
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination0
Federated Adversarial Learning: A Framework with Convergence Analysis0
Federated Adversarial Learning for Robust Autonomous Landing Runway Detection0
Federated Adversarial Training with Transformers0
Federated AI lets a team imagine together: Federated Learning of GANs0
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging0
Federated Analytics: A survey0
Federated Analytics for 6G Networks: Applications, Challenges, and Opportunities0
PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality0
Federated and Differentially Private Learning for Electronic Health Records0
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review0
Federated Learning and Meta Learning: Approaches, Applications, and Directions0
Federated and Privacy-Preserving Learning of Accounting Data in Financial Statement Audits0
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms0
Federated and Transfer Learning for Cancer Detection Based on Image Analysis0
Federated Anomaly Detection over Distributed Data Streams0
Federated Automated Feature Engineering0
Federated Automatic Differentiation0
Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes0
Federated Averaging as Expectation Maximization0
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms0
Federated Bandit: A Gossiping Approach0
Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models0
Federated Bayesian Network Ensembles0
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process0
A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning0
Complex-valued Federated Learning with Differential Privacy and MRI Applications0
Federated Binary Matrix Factorization using Proximal Optimization0
Mitigating Data Absence in Federated Learning Using Privacy-Controllable Data Digests0
Federated brain tumor segmentation: an extensive benchmark0
Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation0
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients0
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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