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 32513300 of 6771 papers

TitleStatusHype
A Framework for Exploring Federated Community Detection0
An Incentive Mechanism for Federated Learning Based on Multiple Resource Exchange0
Venn: Resource Management for Collaborative Learning JobsCode0
Contractive error feedback for gradient compression0
Ensemble Federated Learning: an approach for collaborative pneumonia diagnosis0
Efficient Cross-Domain Federated Learning by MixStyle Approximation0
Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated LearningCode0
Feature Norm Regularized Federated Learning: Transforming Skewed Distributions into Global InsightsCode0
Federated Multilinear Principal Component Analysis with Applications in Prognostics0
Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning0
No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation0
Federated Learning Empowered by Generative Content0
QMGeo: Differentially Private Federated Learning via Stochastic Quantization with Mixed Truncated Geometric Distribution0
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation0
Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering ApproachCode0
Multi-dimensional Fair Federated Learning0
Topology-Based Reconstruction Prevention for Decentralised Learning0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation0
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security0
Privacy-preserving quantum federated learning via gradient hiding0
Improving Communication Efficiency of Federated Distillation via Accumulating Local UpdatesCode0
Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights0
Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity0
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning0
Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data0
FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning0
A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systems0
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance0
TrustFed: A Reliable Federated Learning Framework with Malicious-Attack Resistance0
Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service0
Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge AnchorCode0
The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning0
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature AugmentationCode0
Mitigating Data Injection Attacks on Federated Learning0
Federated Learning is Better with Non-Homomorphic Encryption0
Federated Active Learning for Target Domain GeneralisationCode0
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks0
PROFL: A Privacy-Preserving Federated Learning Method with Stringent Defense Against Poisoning Attacks0
Distributed Bayesian Estimation in Sensor Networks: Consensus on Marginal Densities0
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration0
Exploring the Robustness of Decentralized Training for Large Language Models0
zkDFL: An efficient and privacy-preserving decentralized federated learning with zero-knowledge proof0
FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning0
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach0
FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation0
VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated LearningCode0
Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning0
Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated LearningCode0
Grounding Foundation Models through Federated Transfer Learning: A General Framework0
Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices0
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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