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 15511600 of 6771 papers

TitleStatusHype
Adaptive Distillation for Decentralized Learning from Heterogeneous Clients0
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization0
Distributionally Robust Federated Learning: An ADMM Algorithm0
Data Obfuscation through Latent Space Projection (LSP) for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection0
Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communication0
Detection of Insider Attacks in Distributed Projected Subgradient Algorithms0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications0
Detection of ransomware attacks using federated learning based on the CNN model0
Auction Based Clustered Federated Learning in Mobile Edge Computing System0
De-VertiFL: A Solution for Decentralized Vertical Federated Learning0
A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control0
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks0
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation0
Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things0
Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning0
Device Scheduling for Over-the-Air Federated Learning with Differential Privacy0
Data-Heterogeneous Hierarchical Federated Learning with Mobility0
Device Scheduling with Fast Convergence for Wireless Federated Learning0
Bayesian data fusion with shared priors0
DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning0
DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning0
dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis0
DFML: Decentralized Federated Mutual Learning0
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning0
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning0
Differentially Private AUC Computation in Vertical Federated Learning0
Distributed U-net model and Image Segmentation for Lung Cancer Detection0
Differentially Private CutMix for Split Learning with Vision Transformer0
Differentially Private Data Generative Models0
Bayesian Federated Learning over Wireless Networks0
Differentially Private Distributed Convex Optimization0
Differentially Private Federated Combinatorial Bandits with Constraints0
A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals0
Federated Learning with Uncertainty via Distilled Predictive Distributions0
Differentially Private Federated Learning with Local Regularization and Sparsification0
Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inference0
Differentially Private Federated Learning without Noise Addition: When is it Possible?0
Differentially Private Federated Learning With Time-Adaptive Privacy Spending0
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices0
Differentially Private Federated Learning: A Systematic Review0
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory0
Differentially Private Federated Learning for Resource-Constrained Internet of Things0
Data-Free Evaluation of User Contributions in Federated Learning0
A Two-Stage CAE-Based Federated Learning Framework for Efficient Jamming Detection in 5G Networks0
Differentially Private Federated Learning via Inexact ADMM0
Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates0
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations0
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning0
Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things0
Decentralized Unsupervised Learning of Visual Representations0
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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