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 18511900 of 6771 papers

TitleStatusHype
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware MinimizationCode1
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP OptimizationCode0
Differentially Private Clustered Federated Learning0
Adaptive and Parallel Split Federated Learning in Vehicular Edge ComputingCode1
LoByITFL: Low Communication Secure and Private Federated Learning0
An Innovative Networks in Federated Learning0
Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization0
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew ResilienceCode1
PeerFL: A Simulator for Peer-to-Peer Federated Learning at ScaleCode0
Decentralized Directed Collaboration for Personalized Federated Learning0
Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing0
FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated LearningCode0
Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach0
Efficient Model Compression for Hierarchical Federated Learning0
FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation0
Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous ClientsCode0
LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscation0
Machine learning in business process management: A systematic literature review0
A Systematic Review of Federated Generative Models0
Multi-Level Additive Modeling for Structured Non-IID Federated LearningCode0
Fair Federated Learning under Domain Skew with Local Consistency and Domain DiversityCode1
Secure Hierarchical Federated Learning in Vehicular Networks Using Dynamic Client Selection and Anomaly Detection0
FedSheafHN: Personalized Federated Learning on Graph-structured Data0
Vertical Federated Learning for Effectiveness, Security, Applicability: A SurveyCode1
Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client IndexingCode0
Analytic Federated LearningCode2
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Federated Unsupervised Domain Generalization using Global and Local Alignment of GradientsCode1
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order OptimizationCode1
Exploring Age-of-Information Weighting in Federated Learning under Data Heterogeneity0
CAFe: Cost and Age aware Federated Learning0
Harnessing Increased Client Participation with Cohort-Parallel Federated Learning0
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler0
Unlearning during Learning: An Efficient Federated Machine Unlearning MethodCode1
Decaf: Data Distribution Decompose Attack against Federated Learning0
Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classification0
Towards Client Driven Federated Learning0
DAGER: Exact Gradient Inversion for Large Language ModelsCode1
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated LearningCode0
Thinking Forward: Memory-Efficient Federated Finetuning of Language ModelsCode1
RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure AggregationCode0
Distributed Continual Learning0
Ferrari: Federated Feature Unlearning via Optimizing Feature SensitivityCode1
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data0
Adaptive Gradient Clipping for Robust Federated Learning0
Overcoming the Challenges of Batch Normalization in Federated LearningCode1
Variational Bayes for Federated Continual LearningCode0
Towards Privacy-Aware and Personalised Assistive Robots: A User-Centred Approach0
Recurrent Early Exits for Federated Learning with Heterogeneous ClientsCode1
Worldwide Federated Training of Language Models0
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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