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 40014050 of 6771 papers

TitleStatusHype
FedCC: Robust Federated Learning against Model Poisoning Attacks0
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding0
Security Analysis of SplitFed Learning0
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning0
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model ExtractionCode1
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework0
PGFed: Personalize Each Client's Global Objective for Federated LearningCode0
Self-supervised On-device Federated Learning from Unlabeled Streams0
Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learning0
Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network0
FedALA: Adaptive Local Aggregation for Personalized Federated LearningCode1
Faster Adaptive Federated Learning0
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning0
PiPar: Pipeline Parallelism for Collaborative Machine Learning0
Towards Practical Few-shot Federated NLP0
Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems0
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning0
Hijack Vertical Federated Learning Models As One Party0
Vertical Federated Learning: A Structured Literature Review0
Early prediction of the risk of ICU mortality with Deep Federated LearningCode0
FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning0
On the Design of Communication-Efficient Federated Learning for Health Monitoring0
Privacy-Preserving Federated Deep Clustering based on GAN0
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise0
Performance Evaluation, Optimization and Dynamic Decision in Blockchain Systems: A Recent Overview0
Scalable Hierarchical Over-the-Air Federated Learning0
Flow: Per-Instance Personalized Federated Learning Through Dynamic RoutingCode0
AquaFeL-PSO: A Monitoring System for Water Resources using Autonomous Surface Vehicles based on Multimodal PSO and Federated Learning0
Federated Learning for 5G Base Station Traffic ForecastingCode1
Federated Learning Attacks and Defenses: A Survey0
Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning0
FedSysID: A Federated Approach to Sample-Efficient System IdentificationCode0
MDA: Availability-Aware Federated Learning Client SelectionCode0
Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression0
Inverse Feasibility in Over-the-Air Federated Learning0
FedGS: Federated Graph-based Sampling with Arbitrary Client AvailabilityCode1
Federated Learning Hyper-Parameter Tuning from a System PerspectiveCode0
Knowledge-Aware Federated Active Learning with Non-IID DataCode1
Collaborative Training of Medical Artificial Intelligence Models with non-uniform LabelsCode0
Multi-Job Intelligent Scheduling with Cross-Device Federated Learning0
FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders0
A Dynamic Weighted Federated Learning for Android Malware ClassificationCode1
Event-Triggered Decentralized Federated Learning over Resource-Constrained Edge Devices0
Vertical Federated Learning: Concepts, Advances and Challenges0
Federated Learning on Non-IID Graphs via Structural Knowledge SharingCode1
Fed-TDA: Federated Tabular Data Augmentation on Non-IID DataCode1
GitFL: Adaptive Asynchronous Federated Learning using Version Control0
Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Drift0
Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning0
SPIN: Simulated Poisoning and Inversion Network for Federated Learning-Based 6G Vehicular Networks0
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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