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 66516700 of 6771 papers

TitleStatusHype
Fairness and Privacy Guarantees in Federated Contextual Bandits0
Fairness-aware Agnostic Federated Learning0
Fairness-Aware Client Selection for Federated Learning0
Fairness-aware Federated Learning0
Fairness-Aware Job Scheduling for Multi-Job Federated Learning0
Fairness-Aware Multi-Server Federated Learning Task Delegation over Wireless Networks0
Fairness in Federated Learning: Fairness for Whom?0
Fairness in Federated Learning for Spatial-Temporal Applications0
Fairness in Federated Learning via Core-Stability0
Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System0
Fairness Without Demographics in Human-Centered Federated Learning0
Fair Resource Allocation in Federated Learning0
Faithful Edge Federated Learning: Scalability and Privacy0
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation0
Fake or Compromised? Making Sense of Malicious Clients in Federated Learning0
False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning0
FAMAC: A Federated Assisted Modified Actor-Critic Framework for Secured Energy Saving in 5G and Beyond Networks0
FAM: fast adaptive federated meta-learning0
FaRO 2: an Open Source, Configurable Smart City Framework for Real-Time Distributed Vision and Biometric Systems0
Fast-adapting and Privacy-preserving Federated Recommender System0
Fast Composite Optimization and Statistical Recovery in Federated Learning0
Fast Convergence Algorithm for Analog Federated Learning0
FedAgg: Adaptive Federated Learning with Aggregated Gradients0
Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving0
Fast-Convergent Federated Learning0
Fast-Convergent Federated Learning with Adaptive Weighting0
Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates0
Fast Deep Autoencoder for Federated learning0
Faster Adaptive Federated Learning0
Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization0
Faster Federated Learning with Decaying Number of Local SGD Steps0
Faster federated optimization under second-order similarity0
Faster On-Device Training Using New Federated Momentum Algorithm0
Faster Rates for Compressed Federated Learning with Client-Variance Reduction0
Fast Heterogeneous Federated Learning with Hybrid Client Selection0
FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning0
Fast Server Learning Rate Tuning for Coded Federated Dropout0
FAT: Federated Adversarial Training0
Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks0
Fault Tolerant Serverless VFL Over Dynamic Device Environment0
FAVANO: Federated AVeraging with Asynchronous NOdes0
DrJAX: Scalable and Differentiable MapReduce Primitives in JAX0
FBChain: A Blockchain-based Federated Learning Model with Efficiency and Secure Communication0
FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learning0
FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications0
FDNAS: Improving Data Privacy and Model Diversity in AutoML0
Feasibility Analysis of Federated Neural Networks for Explainable Detection of Atrial Fibrillation0
Feature Aggregation with Latent Generative Replay for Federated Continual Learning of Socially Appropriate Robot Behaviours0
Feature-context driven Federated Meta-Learning for Rare Disease Prediction0
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning0
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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