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 12011250 of 6771 papers

TitleStatusHype
Equitable Federated Learning with Activation Clustering0
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept DriftCode1
FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning0
Adapting MLOps for Diverse In-Network Intelligence in 6G Era: Challenges and Solutions0
LEGO: Language Model Building Blocks0
Towards Active Participant-Centric Vertical Federated Learning: Some Representations May Be All You Need0
Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering0
Securing Federated Learning against Backdoor Threats with Foundation Model Integration0
ProFL: Performative Robust Optimal Federated Learning0
Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning0
Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection0
Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked DataCode1
Deep Learning Aided Broadcast Codes with Feedback0
Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks0
Data Obfuscation through Latent Space Projection (LSP) for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection0
Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis0
Subword Embedding from Bytes Gains Privacy without Sacrificing Accuracy and Complexity0
Extracting Spatiotemporal Data from Gradients with Large Language Models0
DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing0
Distributed Learning for UAV Swarms0
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification0
Geographical Node Clustering and Grouping to Guarantee Data IIDness in Federated Learning0
A Bayesian Framework for Clustered Federated Learning0
Tighter Performance Theory of FedExProx0
DPVS-Shapley:Faster and Universal Contribution Evaluation Component in Federated Learning0
Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer0
FedSpaLLM: Federated Pruning of Large Language Models0
Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning0
Comparative Evaluation of Clustered Federated Learning MethodsCode0
A Communication and Computation Efficient Fully First-order Method for Decentralized Bilevel Optimization0
FedMSE: Federated learning for IoT network intrusion detectionCode1
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning0
Investigating Effective Speaker Property Privacy Protection in Federated Learning for Speech Emotion Recognition0
On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts0
Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach0
Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity0
DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank AdaptationCode0
FedCAP: Robust Federated Learning via Customized Aggregation and PersonalizationCode0
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models0
FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning0
TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic0
Disentangling data distribution for Federated Learning0
Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks0
Age-of-Gradient Updates for Federated Learning over Random Access Channels0
Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study0
Why Go Full? Elevating Federated Learning Through Partial Network UpdatesCode1
Data Quality Control in Federated Instruction-tuning of Large Language Models0
Backdoor Attack on Vertical Federated Graph Neural Network Learning0
FedCCRL: Federated Domain Generalization with Cross-Client Representation LearningCode0
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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