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 12511300 of 6771 papers

TitleStatusHype
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning0
Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
Energy Minimization for Participatory Federated Learning in IoT Analyzed via Game Theory0
The Cost of Local and Global Fairness in Federated LearningCode0
Federated Learning with Differential Privacy: An Utility-Enhanced Approach0
Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data0
Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning0
Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning0
Robust Federated Learning Against Poisoning Attacks: A GAN-Based Defense FrameworkCode0
An Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy0
TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting ModelsCode0
AIGC-assisted Federated Learning for Edge Intelligence: Architecture Design, Research Challenges and Future Directions0
Convergence Theory of Flexible ALADIN for Distributed Optimization0
ProFed: a Benchmark for Proximity-based non-IID Federated Learning0
Unlocking the Value of Decentralized Data: A Federated Dual Learning Approach for Model Aggregation0
MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation0
SAFE: Self-Adjustment Federated Learning Framework for Remote Sensing Collaborative Perception0
Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks0
Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable0
RCC-PFL: Robust Client Clustering under Noisy Labels in Personalized Federated Learning0
Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks0
FedMM-X: A Trustworthy and Interpretable Framework for Federated Multi-Modal Learning in Dynamic Environments0
Federated Learning: A new frontier in the exploration of multi-institutional medical imaging data0
Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for Federated Continual LearningCode0
Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture0
Distributionally Robust Federated Learning: An ADMM Algorithm0
Streaming Federated Learning with Markovian Data0
Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for FCLCode0
FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation0
LoGoFair: Post-Processing for Local and Global Fairness in Federated LearningCode0
A Thorough Assessment of the Non-IID Data Impact in Federated Learning0
FedSAF: A Federated Learning Framework for Enhanced Gastric Cancer Detection and Privacy Preservation0
RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility in Autonomous Vehicles0
Energy-Efficient Federated Learning and Migration in Digital Twin Edge Networks0
Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising0
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data0
Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI0
Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI0
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation0
FedLWS: Federated Learning with Adaptive Layer-wise Weight ShrinkingCode0
Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection0
pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning0
FedBEns: One-Shot Federated Learning based on Bayesian Ensemble0
Federated Continual 3D Segmentation With Single-round Communication0
Online federated learning framework for classification0
RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learning0
Defending Against Gradient Inversion Attacks for Biomedical Images via Learnable Data Perturbation0
Global Group Fairness in Federated Learning via Function TrackingCode0
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions0
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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