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 27512800 of 6771 papers

TitleStatusHype
Federated Learning for UAV-Based Spectrum Sensing: Enhancing Accuracy Through SNR-Weighted Model Aggregation0
Federated Learning for UAV Swarms Under Class Imbalance and Power Consumption Constraints0
Federated Learning for Ultra-Reliable Low-Latency V2V Communications0
Federated Learning in Vehicular Networks0
Communication Efficient Federated Learning for Generalized Linear Bandits0
Federated Learning for Water Consumption Forecasting in Smart Cities0
Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges0
Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks0
A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks0
Fed-Credit: Robust Federated Learning with Credibility Management0
Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation0
Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study0
FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection0
Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis0
Federated Learning from Heterogeneous Data via Controlled Bayesian Air Aggregation0
Federated Learning from Molecules to Processes: A Perspective0
FedCostWAvg: A new averaging for better Federated Learning0
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach0
Communication-Efficient Federated Learning for Neural Machine Translation0
Federated Learning: From Theory to Practice0
A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks0
Active Federated Learning0
A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals0
Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity0
Federated Learning in Adversarial Settings0
Data-Heterogeneous Hierarchical Federated Learning with Mobility0
Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models0
Federated Learning Incentive Mechanism under Buyers' Auction Market0
Communication-Efficient Federated Learning via Quantized Compressed Sensing0
Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data0
Federated learning in food research0
Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review0
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems0
Federated Learning in IoT: a Survey from a Resource-Constrained Perspective0
Model Splitting Enhanced Communication-Efficient Federated Learning for CSI Feedback0
Federated Learning in MIMO Satellite Broadcast System0
Federated Learning in Mobile Edge Computing: An Edge-Learning Perspective for Beyond 5G0
Communication-Efficient Federated Learning via Optimal Client Sampling0
Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database0
Data privacy protection in microscopic image analysis for material data mining0
Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learning0
Federated Learning in Open- and Closed-Loop EMG Decoding: A Privacy and Performance Perspective0
Federated Learning in Practice: Reflections and Projections0
Federated Learning in Satellite Constellations0
Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges0
Federated Learning in Temporal Heterogeneity0
Federated Learning in the Presence of Adversarial Client Unavailability0
Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms0
Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms0
Federated Learning using Smart Contracts on Blockchains, based on Reward Driven 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