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 62516300 of 6771 papers

TitleStatusHype
Compression Boosts Differentially Private Federated Learning0
Mitigating Leakage in Federated Learning with Trusted Hardware0
Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspective0
Federated Learning via Intelligent Reflecting Surface0
Distributed Learning with Low Communication Cost via Gradient Boosting Untrained Neural Network0
Interpretable collaborative data analysis on distributed data0
Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning0
Federated Crowdsensing: Framework and Challenges0
FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural NetworksCode0
Resource-Constrained Federated Learning with Heterogeneous Labels and Models0
FDNAS: Improving Data Privacy and Model Diversity in AutoML0
Collaborative City Digital Twin For Covid-19 Pandemic: A Federated Learning Solution0
BaFFLe: Backdoor detection via Feedback-based Federated Learning0
Local SGD: Unified Theory and New Efficient Methods0
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee0
Semi-supervised Federated Learning for Activity Recognition0
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction0
One-Shot Federated Learning with Neuromorphic Processors0
Empirical Studies of Institutional Federated Learning For Natural Language Processing0
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
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
Differentially-Private Federated Linear BanditsCode0
Hierarchical Federated Learning through LAN-WAN Orchestration0
Network Anomaly Detection Using Federated Learning and Transfer Learning0
GFL: A Decentralized Federated Learning Framework Based On Blockchain0
Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation0
A Federated Learning Approach to Anomaly Detection in Smart Buildings0
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
BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture0
Improving Accuracy of Federated Learning in Non-IID Settings0
COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework0
Can Federated Learning Save The Planet?0
Direct Federated Neural Architecture Search0
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications0
FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers0
Fairness-aware Agnostic Federated Learning0
Voting-based Approaches For Differentially Private Federated Learning0
Optimal Gradient Compression for Distributed and Federated Learning0
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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