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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4751–4800 of 6771 papers

TitleStatusHype
Hierarchical Federated Learning through LAN-WAN Orchestration—0
Hierarchical Federated Learning with Momentum Acceleration in Multi-Tier Networks—0
Hierarchical Federated Learning with Privacy—0
Hierarchical Knowledge Structuring for Effective Federated Learning in Heterogeneous Environments—0
Hierarchically Fair Federated Learning—0
Hierarchical Over-the-Air Federated Edge Learning—0
Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneity—0
Hierarchical Over-the-Air FedGradNorm—0
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks—0
Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design—0
Hierarchical Split Federated Learning: Convergence Analysis and System Optimization—0
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation—0
HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computing—0
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association—0
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning—0
Hijack Vertical Federated Learning Models As One Party—0
Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning—0
HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving—0
Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data—0
Histopathological Image Classification and Vulnerability Analysis using Federated Learning—0
A Historical Trajectory Assisted Optimization Method for Zeroth-Order Federated Learning—0
HLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger Fabric—0
Holdout SGD: Byzantine Tolerant Federated Learning—0
Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning—0
HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning—0
Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case—0
Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum Sharing and Complementing Perspective—0
Horizontal Federated Learning and Secure Distributed Training for Recommendation System with Intel SGX—0
How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?—0
How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?—0
Some Targets Are Harder to Identify than Others: Quantifying the Target-dependent Membership Leakage—0
How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?—0
How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?—0
How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channels—0
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning—0
How to Defend Against Large-scale Model Poisoning Attacks in Federated Learning: A Vertical Solution—0
How To Prevent the Poor Performance Clients for Personalized Federated Learning?—0
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank—0
HPN: Personalized Federated Hyperparameter Optimization—0
HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models—0
HSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting—0
Hubs and Spokes Learning: Efficient and Scalable Collaborative Machine Learning—0
HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated Learning—0
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning—0
Distributed Machine Learning in D2D-Enabled Heterogeneous Networks: Architectures, Performance, and Open Challenges—0
Hybrid Differentially Private Federated Learning on Vertically Partitioned Data—0
Hybrid Federated and Centralized Learning—0
Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network—0
Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data—0
Hybrid Local SGD for Federated Learning with Heterogeneous Communications—0
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All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75—Unverified
2SiloBN + SAMmIoU49.1—Unverified
3SiloBNmIoU45.96—Unverified
4FedSAM + SWAmIoU43.42—Unverified
5FedASAM + SWAmIoU43.02—Unverified
6FedAvg + SWAmIoU42.48—Unverified
7FedASAMmIoU42.27—Unverified
8FedSAMmIoU41.22—Unverified
9FedAvgmIoU38.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32—Unverified
2FedSAM + SWAAcc@1-1262Clients68.12—Unverified
3FedAvg + SWAAcc@1-1262Clients67.52—Unverified
4FedASAMAcc@1-1262Clients64.23—Unverified
5FedSAMAcc@1-1262Clients63.72—Unverified
6FedAvgAcc@1-1262Clients61.91—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64—Unverified
2FedASAMACC@1-100Clients39.76—Unverified
3FedSAM + SWAACC@1-100Clients39.51—Unverified
4FedSAMACC@1-100Clients36.93—Unverified
5FedAvgACC@1-100Clients36.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62—Unverified
2FedASAMACC@1-100Clients40.81—Unverified
3FedSAM + SWAACC@1-100Clients39.24—Unverified
4FedAvgACC@1-100Clients38.59—Unverified
5FedSAMACC@1-100Clients38.56—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72—Unverified
2FedSAM + SWAACC@1-100Clients46.76—Unverified
3FedASAMACC@1-100Clients46.58—Unverified
4FedSAMACC@1-100Clients44.84—Unverified
5FedAvgACC@1-100Clients41.27—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27—Unverified
2FedASAMACC@1-100Clients47.78—Unverified
3FedSAM + SWAACC@1-100Clients46.47—Unverified
4FedSAMACC@1-100Clients46.05—Unverified
5FedAvgACC@1-100Clients42.17—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17—Unverified
2FedSAM + SWAACC@1-100Clients47.96—Unverified
3FedASAMACC@1-100Clients45.61—Unverified
4FedSAMACC@1-100Clients44.73—Unverified
5FedAvgACC@1-100Clients40.43—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01—Unverified
2FedSAM + SWAACC@1-100Clients39.3—Unverified
3FedASAMACC@1-100Clients36.04—Unverified
4FedSAMACC@1-100Clients31.04—Unverified
5FedAvgACC@1-100Clients30.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97—Unverified
2FedASAM + SWAACC@1-100Clients54.79—Unverified
3FedSAM + SWAACC@1-100Clients53.67—Unverified
4FedSAMACC@1-100Clients53.39—Unverified
5FedAvgACC@1-100Clients50.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5—Unverified
2FedSAM + SWAACC@1-100Clients54.36—Unverified
3FedASAM + SWAACC@1-100Clients54.1—Unverified
4FedSAMACC@1-100Clients53.97—Unverified
5FedAvgACC@1-100Clients50.66—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81—Unverified
2FedSAMACC@1-100Clients54.01—Unverified
3FedSAM + SWAACC@1-100Clients53.9—Unverified
4FedASAM + SWAACC@1-100Clients53.86—Unverified
5FedAvgACC@1-100Clients49.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2—Unverified