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 61516200 of 6771 papers

TitleStatusHype
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee0
Semi-supervised Federated Learning for Activity Recognition0
Empirical Studies of Institutional Federated Learning For Natural Language Processing0
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction0
One-Shot Federated Learning with Neuromorphic Processors0
Fast Convergence Algorithm for Analog Federated Learning0
Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework0
Mitigating Backdoor Attacks in Federated Learning0
Federated Learning From Big Data Over NetworksCode0
Spiking Neural Networks -- Part III: Neuromorphic Communications0
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech RecognitionCode1
Optimal Client Sampling for Federated LearningCode1
Optimal Importance Sampling for Federated Learning0
Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms0
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things0
Federated Bandit: A Gossiping Approach0
Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGDCode0
Throughput-Optimal Topology Design for Cross-Silo Federated LearningCode1
Network Anomaly Detection Using Federated Learning and Transfer Learning0
Hierarchical Federated Learning through LAN-WAN Orchestration0
Differentially-Private Federated Linear BanditsCode0
GFL: A Decentralized Federated Learning Framework Based On Blockchain0
Feature Inference Attack on Model Predictions in Vertical Federated LearningCode1
A Federated Learning Approach to Anomaly Detection in Smart Buildings0
Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation0
Federated Bayesian Optimization via Thompson SamplingCode1
Federated Unsupervised Representation Learning0
Sliding Differential Evolution Scheduling for Federated Learning in Bandwidth-Limited Networks0
Layer-wise Characterization of Latent Information Leakage in Federated Learning0
Flow-FL: Data-Driven Federated Learning for Spatio-Temporal Predictions in Multi-Robot Systems0
Federated Learning in Adversarial Settings0
R-GAP: Recursive Gradient Attack on PrivacyCode1
Improving Accuracy of Federated Learning in Non-IID Settings0
FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven MeasureCode1
BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture0
COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework0
Direct Federated Neural Architecture Search0
Can Federated Learning Save The Planet?0
FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers0
Oort: Efficient Federated Learning via Guided Participant SelectionCode1
TextHide: Tackling Data Privacy in Language Understanding TasksCode1
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications0
Federated Learning via Posterior Averaging: A New Perspective and Practical AlgorithmsCode1
Fairness-aware Agnostic Federated Learning0
Voting-based Approaches For Differentially Private Federated Learning0
Optimal Gradient Compression for Distributed and Federated Learning0
Cognitive Learning-Aided Multi-Antenna Communications0
Lower Bounds and Optimal Algorithms for Personalized Federated Learning0
Specialized federated learning using a mixture of expertsCode1
Can we Generalize and Distribute Private Representation Learning?Code0
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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