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 16511700 of 6771 papers

TitleStatusHype
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning0
Age-Based Device Selection and Transmit Power Optimization in Over-the-Air Federated Learning0
Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression RecognitionCode0
LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data0
Population Normalization for Federated Learning0
F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics0
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration0
Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter0
Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution0
Subspace Constraint and Contribution Estimation for Heterogeneous Federated LearningCode0
Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor0
AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models0
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning0
FedCS: Coreset Selection for Federated Learning0
pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation0
HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving0
Federated Deep Subspace Clustering0
Federated Dropout: Convergence Analysis and Resource Allocation0
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing0
Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection0
Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization0
Generalizing in Net-Zero Microgrids: A Study with Federated PPO and TRPOCode0
Enhancing Privacy in Federated Learning through Quantum Teleportation Integration0
Caesar: A Low-deviation Compression Approach for Efficient Federated Learning0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation0
Delayed Random Partial Gradient Averaging for Federated Learning0
Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation0
A Robust Federated Learning Framework for Undependable Devices at Scale0
Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance0
Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical DiagnosisCode0
Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks0
Effective and secure federated online learning to rank0
Optimal Federated Learning for Functional Mean Estimation under Heterogeneous Privacy Constraints0
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach0
FedGIG: Graph Inversion from Gradient in Federated Learning0
Addressing Spatial-Temporal Data Heterogeneity in Federated Continual Learning via Tail Anchor0
An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack0
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis0
GeFL: Model-Agnostic Federated Learning with Generative Models0
Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection in Resource-Limited Settings0
Better Knowledge Enhancement for Privacy-Preserving Cross-Project Defect Prediction0
FedTLU: Federated Learning with Targeted Layer Updates0
Exploiting Label Skewness for Spiking Neural Networks in Federated Learning0
Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine LearningCode0
FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks0
FedCross: Intertemporal Federated Learning Under Evolutionary Games0
Data value estimation on private gradients0
Privacy in Fine-tuning Large Language Models: Attacks, Defenses, and Future Directions0
FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation0
Fed-ZOE: Communication-Efficient Over-the-Air Federated Learning via Zeroth-Order Estimation0
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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