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 58515900 of 6771 papers

TitleStatusHype
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication0
Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications0
Real-time End-to-End Federated Learning: An Automotive Case Study0
Server Averaging for Federated Learning0
Federated Quantum Machine Learning0
A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning0
UAV Communications for Sustainable Federated Learning0
Demystifying the Effects of Non-Independence in Federated Learning0
Semi-Decentralized Federated Learning with Cooperative D2D Local Model AggregationsCode0
A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge LearningCode1
Escaping Saddle Points in Distributed Newton's Method with Communication Efficiency and Byzantine Resilience0
Sample-based Federated Learning via Mini-batch SSCA0
Quantum federated learning through blind quantum computing0
Simeon -- Secure Federated Machine Learning Through Iterative Filtering0
Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions0
SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems0
Auction Based Clustered Federated Learning in Mobile Edge Computing System0
Federated Functional Gradient Boosting0
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data SourcesCode1
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency SpaceCode1
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning0
Personalized Federated Learning using HypernetworksCode1
Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation0
Linear Regression over Networks with Communication Guarantees0
Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked VehiclesCode1
Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systems0
FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite OptimizationCode1
Semi-Supervised Federated Peer Learning for Skin Lesion ClassificationCode0
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data0
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation0
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable DevicesCode1
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated LearningCode0
Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated LearningCode1
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of ThingsCode1
Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning0
PFA: Privacy-preserving Federated Adaptation for Effective Model PersonalizationCode1
ZeroSARAH: Efficient Nonconvex Finite-Sum Optimization with Zero Full Gradient Computation0
Privacy Amplification for Federated Learning via User Sampling and Wireless AggregationCode0
A Theorem of the Alternative for Personalized Federated Learning0
Adversarial training in communication constrained federated learning0
FedPower: Privacy-Preserving Distributed Eigenspace Estimation0
Towards Personalized Federated Learning0
Heterogeneity for the Win: One-Shot Federated ClusteringCode1
Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating0
Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehicles0
Federated Learning without Revealing the Decision Boundaries0
Scalable federated machine learning with FEDnCode1
Constrained Differentially Private Federated Learning for Low-bandwidth Devices0
Efficient Client Contribution Evaluation for Horizontal Federated Learning0
Practical and Private (Deep) Learning without Sampling or ShufflingCode1
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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