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 12011250 of 6771 papers

TitleStatusHype
Secure Distributed On-Device Learning Networks With Byzantine Adversaries0
Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach0
A Modified UDP for Federated Learning Packet Transmissions0
Bayesian Variational Federated Learning and Unlearning in Decentralized Networks0
AMI-FML: A Privacy-Preserving Federated Machine Learning Framework for AMI0
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data0
A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning0
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization0
Data Assetization via Resources-decoupled Federated Learning0
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations0
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice0
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning0
Bayesian Neural Network For Personalized Federated Learning Parameter Selection0
Optimal query complexity for private sequential learning against eavesdropping0
Bayesian Federated Neural Matching that Completes Full Information0
Bayesian Federated Model Compression for Communication and Computation Efficiency0
A Metamodel and Framework for AGI0
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory0
Federated Learning with Uncertainty via Distilled Predictive Distributions0
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning0
Adaptive Parameterization of Deep Learning Models for Federated Learning0
DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLT0
Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing0
Bayesian Federated Learning over Wireless Networks0
A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systems0
Bayesian Federated Learning for Continual Training0
Bayesian Federated Learning: A Survey0
-Weighted Federated Adversarial Training0
Bayesian Federated Inference for estimating Statistical Models based on Non-shared Multicenter Data sets0
Bayesian Federated Cause-of-Death Classification and Quantification Under Distribution Shift0
Almost Tight Error Bounds on Differentially Private Continual Counting0
Bayesian data fusion with shared priors0
A Bayesian Framework for Clustered Federated Learning0
Almost Cost-Free Communication in Federated Best Arm Identification0
Adaptively Weighted Averaging Over-the-Air Computation and Its Application to Distributed Gaussian Process Regression0
Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing0
Bayesian AirComp with Sign-Alignment Precoding for Wireless Federated Learning0
Bayes' capacity as a measure for reconstruction attacks in federated learning0
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning0
BayBFed: Bayesian Backdoor Defense for Federated Learning0
Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning0
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning0
Buffered Asynchronous SGD for Byzantine Learning0
Aligning Beam with Imbalanced Multi-modality: A Generative Federated Learning Approach0
BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning0
A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging0
Accurate Autism Spectrum Disorder prediction using Support Vector Classifier based on Federated Learning (SVCFL)0
Bandwidth Slicing to Boost Federated Learning in Edge Computing0
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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