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

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Showing 5101–5150 of 6771 papers

TitleStatusHype
Meta Knowledge Condensation for Federated Learning—0
Meta-Learning for Federated Face Recognition in Imbalanced Data Regimes—0
Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks—0
MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services—0
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization—0
MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflows—0
Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks—0
MGIC: A Multi-Label Gradient Inversion Attack based on Canny Edge Detection on Federated Learning—0
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis—0
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis—0
Mingling with the Good to Backdoor Federated Learning—0
Minimax Demographic Group Fairness in Federated Learning—0
Minimizing Energy Costs in Deep Learning Model Training: The Gaussian Sampling Approach—0
Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching—0
MIP: CLIP-based Image Reconstruction from PEFT Gradients—0
MISA: Unveiling the Vulnerabilities in Split Federated Learning—0
Mitigating Backdoor Attacks in Federated Learning—0
Mitigating Backdoor Attacks in Federated Learning via Flipping Weight Updates of Low-Activation Input Neurons—0
Mitigating Backdoors in Federated Learning with FLD—0
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning—0
Mitigating Bias in Federated Learning—0
Mitigating Cross-client GANs-based Attack in Federated Learning—0
Mitigating Data Injection Attacks on Federated Learning—0
Differentially Private Clustered Federated Learning—0
Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypes—0
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers—0
Mitigating federated learning contribution allocation instability through randomized aggregation—0
Mitigating Group Bias in Federated Learning for Heterogeneous Devices—0
Mitigating Group-Level Fairness Disparities in Federated Visual Language Models—0
Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity—0
Mitigating Leakage in Federated Learning with Trusted Hardware—0
Mitigating Membership Inference Vulnerability in Personalized Federated Learning—0
Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training—0
Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity—0
Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems—0
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup—0
Mixed Federated Learning: Joint Decentralized and Centralized Learning—0
Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregation—0
Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices—0
Mixed Precision Quantization to Tackle Gradient Leakage Attacks in Federated Learning—0
MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers—0
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation—0
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning—0
MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning—0
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles—0
Mobile Application for Oral Disease Detection using Federated Learning—0
Mobile Augmented Reality with Federated Learning in the Metaverse—0
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices—0
Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks—0
Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification—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