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 33513400 of 6771 papers

TitleStatusHype
Mitigating Bias in Federated Learning0
Mitigating Cross-client GANs-based Attack in Federated Learning0
Mitigating Data Injection Attacks on Federated Learning0
Differentially Private Clustered Federated Learning0
Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypes0
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers0
Mitigating federated learning contribution allocation instability through randomized aggregation0
Mitigating Group Bias in Federated Learning for Heterogeneous Devices0
Mitigating Group-Level Fairness Disparities in Federated Visual Language Models0
Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity0
Mitigating Leakage in Federated Learning with Trusted Hardware0
Mitigating Membership Inference Vulnerability in Personalized Federated Learning0
Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training0
Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity0
Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems0
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup0
Mixed Federated Learning: Joint Decentralized and Centralized Learning0
Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregation0
Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices0
Mixed Precision Quantization to Tackle Gradient Leakage Attacks in Federated Learning0
MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers0
MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation0
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning0
MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning0
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles0
Mobile Application for Oral Disease Detection using Federated Learning0
Mobile Augmented Reality with Federated Learning in the Metaverse0
Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices0
Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks0
Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification0
Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks0
Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Network0
Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning0
Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles0
Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying0
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM0
Modality Alignment Meets Federated Broadcasting0
ModalityMirror: Improving Audio Classification in Modality Heterogeneity Federated Learning with Multimodal Distillation0
Mode Connectivity and Data Heterogeneity of Federated Learning0
Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer0
Model-Driven Quantum Federated Learning (QFL)0
Model Extraction Attacks on Split Federated Learning0
Model Parallelism With Subnetwork Data Parallelism0
Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks0
Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing0
Models of fairness in federated learning0
How to Backdoor HyperNetwork in Personalized Federated Learning?0
Modular Federated Learning0
Modular Federated Learning: A Meta-Framework Perspective0
Momentum Approximation in Asynchronous Private Federated Learning0
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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