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 56515700 of 6771 papers

TitleStatusHype
Rethinking Client Reweighting for Selfish Federated Learning0
Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach0
Diverse Client Selection for Federated Learning via Submodular Maximization0
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions0
RVFR: Robust Vertical Federated Learning via Feature Subspace Recovery0
Distributed Skellam Mechanism: a Novel Approach to Federated Learning with Differential Privacy0
Scalable Robust Federated Learning with Provable Security Guarantees0
Secure Byzantine-Robust Federated Learning with Dimension-free Error0
Demystifying Hyperparameter Optimization in Federated Learning0
Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach0
Certified Robustness for Free in Differentially Private Federated Learning0
Bit-aware Randomized Response for Local Differential Privacy in Federated Learning0
Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective0
A Unified Knowledge Distillation Framework for Deep Directed Graphical Models0
Towards Generalizable Personalized Federated Learning with Adaptive Local Adaptation0
An Agnostic Approach to Federated Learning with Class Imbalance0
-Weighted Federated Adversarial Training0
Agnostic Personalized Federated Learning with Kernel Factorization0
Unsupervised Federated Learning is Possible0
Adversarial Collaborative Learning on Non-IID Features0
Adapt to Adaptation: Learning to Personalize for Cross-Silo Federated Learning0
Accelerating Federated Split Learning via Local-Loss-Based Training0
ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computations0
Federated Learning Algorithms for Generalized Mixed-effects Model (GLMM) on Horizontally Partitioned Data from Distributed Sources0
Private Language Model Adaptation for Speech Recognition0
Federated Deep Learning with Bayesian Privacy0
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization0
MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers0
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation AnalysisCode0
SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning0
Improving Fairness for Data Valuation in Horizontal Federated Learning0
Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance0
Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression0
Enforcing fairness in private federated learning via the modified method of differential multipliers0
Achieving Model Fairness in Vertical Federated LearningCode0
Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching0
Federated Submodel Optimization for Hot and Cold Data FeaturesCode0
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer0
Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting0
Federated Contrastive Learning for Decentralized Unlabeled Medical Images0
Bayesian AirComp with Sign-Alignment Precoding for Wireless Federated Learning0
AMI-FML: A Privacy-Preserving Federated Machine Learning Framework for AMI0
Concept Drift Detection in Federated Networked Systems0
Training Fair Models in Federated Learning without Data Privacy Infringement0
Federated Ensemble Model-based Reinforcement Learning in Edge Computing0
Critical Learning Periods in Federated Learning0
Cost-Effective Federated Learning in Mobile Edge Networks0
FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning0
On the Initial Behavior Monitoring Issues in Federated Learning0
Utility Fairness for the Differentially Private Federated 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