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 14511500 of 6771 papers

TitleStatusHype
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks0
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI0
Decentralized Health Intelligence Network (DHIN)0
Data Poisoning Attacks on Federated Machine Learning0
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities0
Decentralized Low-Rank Fine-Tuning of Large Language Models0
Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum0
Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection0
A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring0
Decentralized Personalized Federated Learning0
Decentralized Personalized Federated Learning for Min-Max Problems0
Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacy0
Decentralized Personalized Online Federated Learning0
Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data0
Federated Continual Learning for Socially Aware Robotics0
A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments0
Data Overvaluation Attack and Truthful Data Valuation in Federated Learning0
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models0
Decoding FL Defenses: Systemization, Pitfalls, and Remedies0
Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement0
Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics0
Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data0
Adaptive Distillation for Decentralized Learning from Heterogeneous Clients0
DEED: A General Quantization Scheme for Communication Efficiency in Bits0
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach0
Deep Convolutional Neural Networks for Short-Term Multi-Energy Demand Prediction of Integrated Energy Systems0
Information-Geometric Barycenters for Bayesian Federated Learning0
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization0
Deep Equilibrium Models Meet Federated Learning0
Deep Federated Anomaly Detection for Multivariate Time Series Data0
Differentially Private Meta-Learning0
Backdoor Attack on Vertical Federated Graph Neural Network Learning0
Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information0
Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling0
Deep leakage from gradients0
Data Obfuscation through Latent Space Projection (LSP) for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection0
Auction Based Clustered Federated Learning in Mobile Edge Computing System0
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency0
Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks0
Deep Learning Model Security: Threats and Defenses0
Backdoor Attacks in Peer-to-Peer Federated Learning0
A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control0
Adaptive Federated Pruning in Hierarchical Wireless Networks0
Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings0
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT0
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets0
Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing0
DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection0
Data-Heterogeneous Hierarchical Federated Learning with Mobility0
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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