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 47014750 of 6771 papers

TitleStatusHype
Group privacy for personalized federated learning0
GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization0
GTV: Generating Tabular Data via Vertical Federated Learning0
Boosting Resource-Constrained Federated Learning Systems with Guessed Updates0
HADFL: Heterogeneity-aware Decentralized Federated Learning Framework0
HAFLO: GPU-Based Acceleration for Federated Logistic Regression0
Fairness-aware Federated Minimax Optimization with Convergence Guarantee0
Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor0
FEATHERS: Federated Architecture and Hyperparameter Search0
Harmonizing Generalization and Personalization in Ring-topology Decentralized Federated Learning0
Harnessing Increased Client Participation with Cohort-Parallel Federated Learning0
Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning0
Harvesting Private Medical Images in Federated Learning Systems with Crafted Models0
Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning0
HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection0
Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning0
HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning0
Heterogeneity: An Open Challenge for Federated On-board Machine Learning0
Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression0
Heterogeneity-Aware Coordination for Federated Learning via Stitching Pre-trained blocks0
Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data0
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning0
Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning0
Heterogeneity Matters even More in Distributed Learning: Study from Generalization Perspective0
Heterogeneous Data-Aware Federated Learning0
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning0
Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning0
Heterogeneous Federated Learning0
Heterogeneous Federated Learning on a Graph0
Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction0
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism0
Heterogeneous Federated Learning Using Knowledge Codistillation0
Heterogeneous Federated Learning via Personalized Generative Networks0
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training0
Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks0
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models0
HEX: Human-in-the-loop Explainability via Deep Reinforcement Learning0
HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast0
H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications0
HF-Fed: Hierarchical based customized Federated Learning Framework for X-Ray Imaging0
H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning0
H-FLTN: A Privacy-Preserving Hierarchical Framework for Electric Vehicle Spatio-Temporal Charge Prediction0
Hidden Data Privacy Breaches in Federated Learning0
HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask0
Hierarchical and Decentralised Federated Learning0
Hierarchical Bayesian Bandits0
Hierarchical Federated ADMM0
Hierarchical Federated Learning Incentivization for Gas Usage Estimation0
Hierarchical Federated Learning Across Heterogeneous Cellular Networks0
Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity0
Show:102550
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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