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 28012850 of 6771 papers

TitleStatusHype
Mobile Application for Oral Disease Detection using Federated Learning0
Single-shot General Hyper-parameter Optimization for Federated Learning0
EcoLearn: Optimizing the Carbon Footprint of Federated Learning0
Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioning0
Contextual Stochastic Bilevel Optimization0
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated LearningCode1
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing0
Taming Gradient Variance in Federated Learning with Networked Control Variates0
Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object DetectionCode1
Secure short-term load forecasting for smart grids with transformer-based federated learning0
Personalized Federated X -armed Bandit0
AirFL-Mem: Improving Communication-Learning Trade-Off by Long-Term Memory0
How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channels0
Information-Theoretic Generalization Analysis for Topology-aware Heterogeneous Federated Edge Learning over Noisy Channels0
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning0
FLTrojan: Privacy Leakage Attacks against Federated Language Models Through Selective Weight Tampering0
Accelerating Split Federated Learning over Wireless Communication Networks0
ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease0
Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms0
Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation0
Serverless Federated Learning with flwr-serverlessCode1
Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey0
Dynamically Weighted Federated k-MeansCode0
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive OptimizationCode1
Federated learning compression designed for lightweight communicationsCode0
Coordinated Replay Sample Selection for Continual Federated Learning0
Quantum Federated Learning With Quantum Networks0
An Efficient Imbalance-Aware Federated Learning Approach for Wearable Healthcare with Autoregressive Ratio Observation0
B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction0
FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients0
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification0
Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis0
PPFL: A Personalized Federated Learning Framework for Heterogeneous Population0
Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices0
Goal-oriented Communications for the IoT: System Design and Adaptive Resource Optimization0
The Hidden Adversarial Vulnerabilities of Medical Federated Learning0
Can We Trust the Similarity Measurement in Federated Learning?0
Competitive Advantage Attacks to Decentralized Federated Learning0
FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food FlowsCode0
BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model0
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset0
VFedMH: Vertical Federated Learning for Training Multiple Heterogeneous Models0
FLTracer: Accurate Poisoning Attack Provenance in Federated LearningCode1
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning0
An Efficient Federated Learning Framework for Training Semantic Communication System0
A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs0
Equipping Federated Graph Neural Networks with Structure-aware Group FairnessCode0
Effective and Efficient Federated Tree Learning on Hybrid Data0
On the Distributed Evaluation of Generative Models0
Fact-based Agent modeling for Multi-Agent Reinforcement 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