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 851900 of 6771 papers

TitleStatusHype
A Survey on Federated Learning in Human Sensing0
A study on performance limitations in Federated Learning0
Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation PerspectiveCode0
FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image ClassificationCode0
Over-the-Air Fair Federated Learning via Multi-Objective OptimizationCode0
Multi-Modal One-Shot Federated Ensemble Learning for Medical Data with Vision Large Language Model0
The Robustness of Spiking Neural Networks in Federated Learning with Compression Against Non-omniscient Byzantine Attacks0
From Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning0
Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation0
Proof-of-Data: A Consensus Protocol for Collaborative Intelligence0
CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks0
AFed: Algorithmic Fair Federated Learning0
Incentive-Compatible Federated Learning with Stackelberg Game Modeling0
FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models0
Efficient Deployment of Large Language Models on Resource-constrained Devices0
Diffusion Model-Based Data Synthesis Aided Federated Semi-Supervised Learning0
Mingling with the Good to Backdoor Federated Learning0
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning0
LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data0
Stackelberg Game Based Performance Optimization in Digital Twin Assisted Federated Learning over NOMA Networks0
Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learning0
Denoising and Adaptive Online Vertical Federated Learning for Sequential Multi-Sensor Data in Industrial Internet of Things0
Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression RecognitionCode0
Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution0
F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics0
Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor0
Subspace Constraint and Contribution Estimation for Heterogeneous Federated LearningCode0
Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter0
Population Normalization for Federated Learning0
HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving0
FedCS: Coreset Selection for Federated Learning0
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning0
pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation0
AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models0
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration0
Federated Deep Subspace Clustering0
Federated Dropout: Convergence Analysis and Resource Allocation0
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing0
Generalizing in Net-Zero Microgrids: A Study with Federated PPO and TRPOCode0
Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization0
Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection0
Enhancing Privacy in Federated Learning through Quantum Teleportation Integration0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation0
Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance0
Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation0
Caesar: A Low-deviation Compression Approach for Efficient Federated Learning0
Federated Unlearning with Gradient Descent and Conflict MitigationCode1
Delayed Random Partial Gradient Averaging for Federated Learning0
Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised LearningCode3
A Robust Federated Learning Framework for Undependable Devices at Scale0
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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