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 15511600 of 6771 papers

TitleStatusHype
A Unified Analysis of Stochastic Gradient Descent with Arbitrary Data Permutations and Beyond0
Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data0
Measuring Heterogeneity in Machine Learning with Distributed Energy Distance0
Integrating Personalized Federated Learning with Control Systems for Enhanced Performance0
Decentralized Low-Rank Fine-Tuning of Large Language Models0
FedAlign: Federated Domain Generalization with Cross-Client Feature Alignment0
Towards Distributed Backdoor Attacks with Network Detection in Decentralized Federated Learning0
A Two-Stage CAE-Based Federated Learning Framework for Efficient Jamming Detection in 5G Networks0
Pre-trained Model Guided Mixture Knowledge Distillation for Adversarial Federated Learning0
Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case0
Efficient Client Selection in Federated Learning0
A Post-Processing-Based Fair Federated Learning Framework0
Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models0
Federated Domain Generalization with Data-free On-server Matching Gradient0
Data Assetization via Resources-decoupled Federated Learning0
Optimal Strategies for Federated Learning Maintaining Client Privacy0
AirTOWN: A Privacy-Preserving Mobile App for Real-time Pollution-Aware POI Suggestion0
Unlearning Clients, Features and Samples in Vertical Federated Learning0
Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models0
FedPref: Federated Learning Across Heterogeneous Multi-objective PreferencesCode0
PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy0
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach0
FedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling0
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning0
A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning0
Practical quantum federated learning and its experimental demonstration0
FedCLEAN: byzantine defense by CLustering Errors of Activation maps in Non-IID federated learning environments0
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism0
Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction0
MyDigiTwin: A Privacy-Preserving Framework for Personalized Cardiovascular Risk Prediction and Scenario ExplorationCode0
Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL0
CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning0
Communication-Efficient and Privacy-Adaptable Mechanism for Federated LearningCode0
Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy0
Federated Learning with Sample-level Client Drift Mitigation0
The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions0
Sparse Incremental Aggregation in Satellite Federated Learning0
Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks0
Trustformer: A Trusted Federated Transformer0
Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching0
A Novel Pearson Correlation-Based Merging Algorithm for Robust Distributed Machine Learning with Heterogeneous Data0
Federated Testing (FedTest): A New Scheme to Enhance Convergence and Mitigate Adversarial Attacks in Federating Learning0
Federated Deep Reinforcement Learning for Energy Efficient Multi-Functional RIS-Assisted Low-Earth Orbit Networks0
Synesthesia of Machines (SoM)-Aided FDD Precoding with Sensing Heterogeneity: A Vertical Federated Learning Approach0
Temporal Analysis of Adversarial Attacks in Federated Learning0
pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup0
Efficient Transmission of Radiomaps via Physics-Enhanced Semantic Communications0
Distributed Quasi-Newton Method for Fair and Fast Federated Learning0
UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharing0
Multi-Task Over-the-Air Federated Learning in Cell-Free Massive MIMO Systems0
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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