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 17511800 of 6771 papers

TitleStatusHype
Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue0
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime0
Data, Competition, and Digital Platforms0
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning0
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration0
Learning to Prompt Your Domain for Vision-Language Models0
Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments0
Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selection0
Adaptive Differential Privacy in Federated Learning: A Priority-Based Approach0
DVFL: A Vertical Federated Learning Method for Dynamic Data0
DWFL: Enhancing Federated Learning through Dynamic Weighted Averaging0
Elastic Aggregation for Federated Optimization0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach0
A Blockchain Solution for Collaborative Machine Learning over IoT0
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning0
Dynamic Attention-based Communication-Efficient Federated Learning0
Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings0
Data Assetization via Resources-decoupled Federated Learning0
Dynamic Clustering in Federated Learning0
Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection0
Dynamic Differential-Privacy Preserving SGD0
Dynamic Fair Federated Learning Based on Reinforcement Learning0
Dynamic Federated Learning0
Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles0
Strategic Federated Learning: Application to Smart Meter Data Clustering0
DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learning0
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations0
Dynamic Fusion based Federated Learning for COVID-19 Detection0
Dynamic Gradient Aggregation for Federated Domain Adaptation0
Dynamic Heterogeneous Federated Learning with Multi-Level Prototypes0
Dynamic Network-Assisted D2D-Aided Coded Distributed Learning0
Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives0
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies0
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning0
Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning0
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset0
E2FL: Equal and Equitable Federated Learning0
An Empirical Evaluation of Federated Contextual Bandit Algorithms0
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey0
Epidemic Decision-making System Based Federated Reinforcement Learning0
Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach0
Efficient Vertical Federated Learning with Secure Aggregation0
Eavesdrop the Composition Proportion of Training Labels in Federated Learning0
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning0
Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model0
Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices0
EdgeAgentX: A Novel Framework for Agentic AI at the Edge in Military Communication Networks0
Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection in Resource-Limited Settings0
Edge-assisted Democratized Learning Towards Federated Analytics0
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach0
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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