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 53015350 of 6771 papers

TitleStatusHype
On-device Federated Learning with Flower0
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning0
On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations0
One Gradient Frank-Wolfe for Decentralized Online Convex and Submodular Optimization0
One Node Per User: Node-Level Federated Learning for Graph Neural Networks0
One-Shot Clustering for Federated Learning0
One-shot Empirical Privacy Estimation for Federated Learning0
One-Shot Federated Learning0
One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes0
A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments0
One-Shot Federated Learning with Bayesian Pseudocoresets0
One-Shot Federated Learning with Classifier-Guided Diffusion Models0
One-Shot Federated Learning with Classifier-Free Diffusion Models0
One-Shot Federated Learning with Neuromorphic Processors0
One-Shot Federated Unsupervised Domain Adaptation with Scaled Entropy Attention and Multi-Source Smoothed Pseudo Labeling0
On Feasibility of Server-side Backdoor Attacks on Split Learning0
On Federated Learning of Deep Networks from Non-IID Data: Parameter Divergence and the Effects of Hyperparametric Methods0
On Federated Learning with Energy Harvesting Clients0
On Global Convergence Rates for Federated Policy Gradient under Heterogeneous Environment0
On Heterogeneously Distributed Data, Sparsity Matters0
On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learning0
On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps0
On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions0
On Large-Cohort Training for Federated Learning0
Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraint0
Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning0
To Store or Not? Online Data Selection for Federated Learning with Limited Storage0
Online Distributed Learning with Quantized Finite-Time Coordination0
Online federated learning framework for classification0
Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Drift0
Online Federation For Mixtures of Proprietary Agents with Black-Box Encoders0
Online Learning with Adversaries: A Differential-Inclusion Analysis0
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction0
Online Mirror Descent for Tchebycheff Scalarization in Multi-Objective Optimization0
Online Model Compression for Federated Learning with Large Models0
Online-Score-Aided Federated Learning: Taming the Resource Constraints in Wireless Networks0
Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting0
Online Vertical Federated Learning for Cooperative Spectrum Sensing0
Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation0
On Model Protection in Federated Learning against Eavesdropping Attacks0
On Principled Local Optimization Methods for Federated Learning0
On Sampling Strategies for Spectral Model Sharding0
On Second-order Optimization Methods for Federated Learning0
Towards Federated RLHF with Aggregated Client Preference for LLMs0
On the Convergence of a Federated Expectation-Maximization Algorithm0
On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients0
On the Convergence of Federated Averaging with Cyclic Client Participation0
On the Convergence of FedProx with Extrapolation and Inexact Prox0
On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning0
On the Convergence of Local Descent Methods in 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