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 651700 of 6771 papers

TitleStatusHype
Orthogonal Calibration for Asynchronous Federated Learning0
FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities0
Accurate Forgetting for Heterogeneous Federated Continual LearningCode0
Distributed U-net model and Image Segmentation for Lung Cancer Detection0
VFL-RPS: Relevant Participant Selection in Vertical Federated Learning0
Federated Fine-Tuning of Large Language Models: Kahneman-Tversky vs. Direct Preference Optimization0
Vision Foundation Models in Medical Image Analysis: Advances and Challenges0
Model Inversion Attack against Federated Unlearning0
Blockchain-based Framework for Scalable and Incentivized Federated Learning0
Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum Sharing and Complementing Perspective0
Smoothed Normalization for Efficient Distributed Private Optimization0
Federated Variational Inference for Bayesian Mixture Models0
Fluid Antenna Enabled Over-the-Air Federated Learning: Joint Optimization of Positioning, Beamforming, and User Selection0
A new framework for prognostics in decentralized industries: Enhancing fairness, security, and transparency through Blockchain and Federated Learning0
FedEAT: A Robustness Optimization Framework for Federated LLMs0
Double Momentum and Error Feedback for Clipping with Fast Rates and Differential Privacy0
Ten Challenging Problems in Federated Foundation Models0
Efficient Zero-Order Federated Finetuning of Language Models for Resource-Constrained Devices0
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis0
Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities0
Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting0
Vertical Federated Continual Learning via Evolving Prototype Knowledge0
One-shot Federated Learning Methods: A Practical GuideCode0
Use of Air Quality Sensor Network Data for Real-time Pollution-Aware POI SuggestionCode0
Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodology0
RLSA-PFL: Robust Lightweight Secure Aggregation with Model Inconsistency Detection in Privacy-Preserving Federated Learning0
Representation Learning to Advance Multi-institutional Studies with Electronic Health Record Data0
FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated LearningCode0
Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly0
One-Shot Federated Learning with Classifier-Free Diffusion Models0
PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated LearningCode0
FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge DevicesCode0
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy0
Optimizing Asynchronous Federated Learning: A~Delicate Trade-Off Between Model-Parameter Staleness and Update Frequency0
SLVR: Securely Leveraging Client Validation for Robust Federated Learning0
An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language ModelsCode0
PFedDST: Personalized Federated Learning with Decentralized Selection Training0
FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data0
Unveiling Client Privacy Leakage from Public Dataset Usage in Federated Distillation0
Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning0
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation0
Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT0
Many-Task Federated Fine-Tuning via Unified Task Vectors0
Federated Continual Learning: Concepts, Challenges, and Solutions0
Krum Federated Chain (KFC): Using blockchain to defend against adversarial attacks in Federated LearningCode0
Analytic Personalized Federated Meta-Learning0
Federated Sinkhorn0
Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Game0
Fine-tuning Multimodal Transformers on Edge: A Parallel Split Learning Approach0
DROP: Poison Dilution via Knowledge Distillation for Federated LearningCode0
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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