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 59015950 of 6771 papers

TitleStatusHype
Understanding Unintended Memorization in Language Models Under Federated Learning0
An Investigation towards Differentially Private Sequence Tagging in a Federated Framework0
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning0
Unifying Distillation with Personalization in Federated Learning0
Towards a Federated Learning Framework for Heterogeneous Devices of Internet of Things0
Federated Learning for Industrial Internet of Things in Future Industries0
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax ProblemsCode0
Concept drift detection and adaptation for federated and continual learning0
Federated Learning for Short-term Residential Load Forecasting0
Clustered Federated Learning via Generalized Total Variation MinimizationCode0
Federated Graph Learning -- A Position Paper0
Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications0
Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection0
Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis0
DoStoVoQ: Doubly Stochastic Voronoi Vector Quantization SGD for Federated Learning0
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning0
Prototype Guided Federated Learning of Visual Feature Representations0
User-Level Label Leakage from Gradients in Federated LearningCode0
A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated LearningCode0
Separation of Powers in Federated Learning0
DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities0
DRIVE: One-bit Distributed Mean EstimationCode0
Towards Unsupervised Domain Adaptation for Deep Face Recognition under Privacy Constraints via Federated Learning0
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment0
OpenFL: An open-source framework for Federated Learning0
The FeatureCloud AI Store for Federated Learning in Biomedicine and BeyondCode0
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis0
Federated Unbiased Learning to Rank0
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism DesignCode0
Latency Analysis of Consortium Blockchained Federated Learning0
Slashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates0
Loss Tolerant Federated LearningCode0
A Hybrid Architecture for Federated and Centralized Learning0
FedGL: Federated Graph Learning Framework with Global Self-Supervision0
Federated Face Recognition0
Membership Inference Attacks on Deep Regression Models for Neuroimaging0
Byzantine-Robust and Privacy-Preserving Framework for FedML0
Federated Multi-View Learning for Private Medical Data Integration and Analysis0
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX0
OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning0
Convergence Analysis and System Design for Federated Learning over Wireless Networks0
Privacy-Preserving Federated Learning on Partitioned Attributes0
From Distributed Machine Learning to Federated Learning: A Survey0
Cluster-driven Graph Federated Learning over Multiple Domains0
Towards Fair Federated Learning with Zero-Shot Data Augmentation0
Secure and Efficient Federated Learning Through Layering and Sharding Blockchain0
Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach0
Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning0
Simultaneous Wireless Information and Power Transfer for Federated Learning0
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data0
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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