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 55015550 of 6771 papers

TitleStatusHype
A Survey on the Role of Artificial Intelligence and Machine Learning in 6G-V2X Applications0
A Survey on Vertical Federated Learning: From a Layered Perspective0
Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective0
Asymmetrically Decentralized Federated Learning0
Asymmetrical Vertical Federated Learning0
Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation0
AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms0
Asynchronous Byzantine Federated Learning0
Asynchronous Collaborative Learning Across Data Silos0
Asynchronous Decentralized Federated Learning for Collaborative Fault Diagnosis of PV Stations0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging0
Asynchronous Diffusion Learning with Agent Subsampling and Local Updates0
Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation0
Asynchronous Federated Learning for Sensor Data with Concept Drift0
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence0
Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation0
Asynchronous Federated Learning with Incentive Mechanism Based on Contract Theory0
Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise0
Asynchronous Federated Stochastic Optimization for Heterogeneous Objectives Under Arbitrary Delays0
Asynchronous Hierarchical Federated Learning0
Asynchronous Local Computations in Distributed Bayesian Learning0
Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization0
Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients0
Asynchronous Online Federated Learning for Edge Devices with Non-IID Data0
Asynchronous Online Federated Learning with Reduced Communication Requirements0
Asynchronous Personalized Federated Learning through Global Memorization0
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits0
Asynchronous Wireless Federated Learning with Probabilistic Client Selection0
A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples0
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization0
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior0
A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles0
A Systematic Literature Review on Client Selection in Federated Learning0
A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective0
A Systematic Literature Review on Federated Learning: From A Model Quality Perspective0
A Systematic Review of Federated Generative Models0
A Theorem of the Alternative for Personalized Federated Learning0
A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning0
A Thorough Assessment of the Non-IID Data Impact in Federated Learning0
ATM: Improving Model Merging by Alternating Tuning and Merging0
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework0
A Trustworthy AIoT-enabled Localization System via Federated Learning and Blockchain0
ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems0
A Tutorial of Personalized Federated Recommender Systems: Recent Advances and Future Directions0
A Two-Stage CAE-Based Federated Learning Framework for Efficient Jamming Detection in 5G Networks0
A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals0
A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control0
Auction Based Clustered Federated Learning in Mobile Edge Computing System0
Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement0
Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacy0
Show:102550
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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