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 26012650 of 6771 papers

TitleStatusHype
3FM: Multi-modal Meta-learning for Federated TasksCode0
Privacy-Aware Document Visual Question AnsweringCode1
Dynamic Heterogeneous Federated Learning with Multi-Level Prototypes0
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
Greedy Shapley Client Selection for Communication-Efficient Federated LearningCode1
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey0
A Framework for Exploring Federated Community Detection0
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
Contractive error feedback for gradient compression0
An Incentive Mechanism for Federated Learning Based on Multiple Resource Exchange0
Venn: Resource Management for Collaborative Learning JobsCode0
Feature Norm Regularized Federated Learning: Transforming Skewed Distributions into Global InsightsCode0
Ensemble Federated Learning: an approach for collaborative pneumonia diagnosis0
Language-Guided Transformer for Federated Multi-Label ClassificationCode1
Efficient Cross-Domain Federated Learning by MixStyle Approximation0
Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated LearningCode0
Federated Multilinear Principal Component Analysis with Applications in Prognostics0
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 KilobytesCode1
Exploiting Label Skews in Federated Learning with Model ConcatenationCode1
Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide ImagesCode1
No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation0
Federated Learning Empowered by Generative Content0
Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning0
QMGeo: Differentially Private Federated Learning via Stochastic Quantization with Mixed Truncated Geometric Distribution0
Fake It Till Make It: Federated Learning with Consensus-Oriented Generation0
Multi-dimensional Fair Federated Learning0
Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering ApproachCode0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation0
PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and BenchmarkCode4
Topology-Based Reconstruction Prevention for Decentralised Learning0
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security0
Improving Communication Efficiency of Federated Distillation via Accumulating Local UpdatesCode0
Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity0
Privacy-preserving quantum federated learning via gradient hiding0
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning0
Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights0
Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data0
FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning0
TrustFed: A Reliable Federated Learning Framework with Malicious-Attack Resistance0
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
Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service0
The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning0
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain ShiftsCode1
Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge AnchorCode0
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks0
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature AugmentationCode0
Mitigating Data Injection Attacks on Federated Learning0
Federated Active Learning for Target Domain GeneralisationCode0
Federated Learning is Better with Non-Homomorphic Encryption0
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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