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 34013450 of 6771 papers

TitleStatusHype
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning0
Federated Empirical Risk Minimization via Second-Order Method0
Federated Conformal Predictors for Distributed Uncertainty QuantificationCode1
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices0
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning0
Secure Vertical Federated Learning Under Unreliable Connectivity0
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New AlgorithmsCode0
A Framework for Incentivized Collaborative Learning0
Parameter-Efficient Fine-Tuning without Introducing New LatencyCode0
Distributed Trust Through the Lens of Software Architecture0
Federated Neural Compression Under Heterogeneous Data0
FAVANO: Federated AVeraging with Asynchronous NOdes0
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization0
Federated Composite Saddle Point Optimization0
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning0
Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks0
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training0
Theoretically Principled Federated Learning for Balancing Privacy and Utility0
Stochastic Unrolled Federated LearningCode0
Local SGD Accelerates Convergence by Exploiting Second Order Information of the Loss Function0
FedZero: Leveraging Renewable Excess Energy in Federated LearningCode1
CorrFL: Correlation-based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT EnvironmentCode0
Federated Prompt Learning for Weather Foundation Models on DevicesCode1
Fair Differentially Private Federated Learning Framework0
Federated Variational Inference: Towards Improved Personalization and Generalization0
Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization0
Explicit Personalization and Local Training: Double Communication Acceleration in Federated LearningCode0
When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User InteractionsCode0
Federated Learning of Medical Concepts Embedding using BEHRTCode0
One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes0
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with AdapterCode1
Confidence-aware Personalized Federated Learning via Variational Expectation MaximizationCode1
Can Public Large Language Models Help Private Cross-device Federated Learning?0
Privacy in Multimodal Federated Human Activity Recognition0
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth LandscapeCode1
Goal-Oriented Communications in Federated Learning via Feedback on Risk-Averse Participation0
Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning MethodsCode0
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection0
PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy0
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models0
Trustworthy Federated Learning: A Survey0
Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languages0
Efficient Vertical Federated Learning with Secure Aggregation0
Convergence Analysis of Over-the-Air FL with Compression and Power Control via Clipping0
Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination0
Mitigating Group Bias in Federated Learning: Beyond Local FairnessCode0
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime0
Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients0
Faster Federated Learning with Decaying Number of Local SGD Steps0
Smart Policy Control for Securing Federated Learning Management System0
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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