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 51515200 of 6771 papers

TitleStatusHype
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data0
Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning0
Divergence-aware Federated Self-Supervised LearningCode0
Federated Unsupervised Domain Adaptation for Face Recognition0
HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection0
Global Update Guided Federated Learning0
CD^2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning0
Decentralized Event-Triggered Federated Learning with Heterogeneous Communication ThresholdsCode0
FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity0
FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance ImprovementCode0
Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation0
Federated Self-supervised Speech Representations: Are We There Yet?0
DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg0
Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks0
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications0
Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryption0
FedSynth: Gradient Compression via Synthetic Data in Federated Learning0
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning0
Byzantine-Robust Federated Linear Bandits0
FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging0
Optimising Communication Overhead in Federated Learning Using NSGA-II0
Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation0
Accelerating Federated Edge Learning via Topology Optimization0
Scaling Language Model Size in Cross-Device Federated Learning0
Differentially Private Federated Learning via Reconfigurable Intelligent SurfaceCode0
Federated Learning for the Classification of Tumor Infiltrating Lymphocytes0
Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination0
Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated LearningCode0
Federated Domain Adaptation for ASR with Full Self-Supervision0
Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data0
Federated Learning-Based Localization with Heterogeneous Fingerprint Database0
FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation0
Federated Learning with Position-Aware Neurons0
Knowledge-Guided Learning for Transceiver Design in Over-the-Air Federated Learning0
Federated Named Entity Recognition0
Adversarial Representation Sharing: A Quantitative and Secure Collaborative Learning Framework0
Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point0
SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks0
Sparse Federated Learning with Hierarchical Personalized Models0
ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation0
A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study0
SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learning0
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning0
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization0
Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching0
Efficient Fully Distributed Federated Learning with Adaptive Local Links0
Asynchronous Collaborative Learning Across Data Silos0
Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing0
Federated Self-Supervised Learning for Acoustic Event Classification0
Feature Distribution Matching for Federated Domain GeneralizationCode0
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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