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
A-LAQ: Adaptive Lazily Aggregated Quantized Gradient0
Declarative Privacy-Preserving Inference Queries0
Algorithm Fairness in AI for Medicine and Healthcare0
A Life-long Learning Intrusion Detection System for 6G-Enabled IoV0
A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging0
Aligning Beam with Imbalanced Multi-modality: A Generative Federated Learning Approach0
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems0
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning0
Almost Cost-Free Communication in Federated Best Arm Identification0
Almost Tight Error Bounds on Differentially Private Continual Counting0
-Weighted Federated Adversarial Training0
A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systems0
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning0
A Metamodel and Framework for AGI0
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice0
AMI-FML: A Privacy-Preserving Federated Machine Learning Framework for AMI0
A Modified UDP for Federated Learning Packet Transmissions0
(Amplified) Banded Matrix Factorization: A unified approach to private training0
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning0
AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling0
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning0
A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications0
A Multi-Modal Federated Learning Framework for Remote Sensing Image Classification0
A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning0
A Multivocal Literature Review on Privacy and Fairness in Federated Learning0
An Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learning0
An Adaptive Differential Privacy Method Based on Federated Learning0
An advanced data fabric architecture leveraging homomorphic encryption and federated learning0
An Aggregation-Free Federated Learning for Tackling Data Heterogeneity0
An Agnostic Approach to Federated Learning with Class Imbalance0
Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach0
Analysing the Influence of Attack Configurations on the Reconstruction of Medical Images in Federated Learning0
Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data0
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity0
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression0
Analysis of regularized federated learning0
Analysis of Total Variation Minimization for Clustered Federated Learning0
Analytic Personalized Federated Meta-Learning0
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing0
Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario0
Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks0
An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems0
Anarchic Federated Learning0
Anatomical 3D Style Transfer Enabling Efficient Federated Learning with Extremely Low Communication Costs0
An Auction-based Marketplace for Model Trading in Federated Learning0
An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning0
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee0
An Efficient and Multi-private Key Secure Aggregation for Federated Learning0
An Efficient and Robust System for Vertically Federated Random Forest0
An Efficient Imbalance-Aware Federated Learning Approach for Wearable Healthcare with Autoregressive Ratio Observation0
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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