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 34513500 of 6771 papers

TitleStatusHype
Multi-Session Budget Optimization for Forward Auction-based Federated Learning0
Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare0
Multi-Source to Multi-Target Decentralized Federated Domain Adaptation0
Multi-Target Federated Backdoor Attack Based on Feature Aggregation0
Multi-Task and Transfer Learning for Federated Learning Applications0
Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation0
Multi-task Federated Edge Learning (MtFEEL) in Wireless Networks0
Multi-task Federated Learning for Heterogeneous Pancreas Segmentation0
Multi-task Federated Learning with Encoder-Decoder Structure: Enabling Collaborative Learning Across Different Tasks0
Over-the-Air Federated Multi-Task Learning0
Multi-Task Over-the-Air Federated Learning in Cell-Free Massive MIMO Systems0
Multi-Task System Identification of Similar Linear Time-Invariant Dynamical Systems0
Multi-Tier Client Selection for Mobile Federated Learning Networks0
Multi-Tier Federated Learning for Vertically Partitioned Data0
Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners0
Mutual Information Regularization for Vertical Federated Learning0
MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones0
MYSTIKO : : Cloud-Mediated, Private, Federated Gradient Descent0
Navigating Distribution Shifts in Medical Image Analysis: A Survey0
Navigating Heterogeneity and Privacy in One-Shot Federated Learning with Diffusion Models0
Navigating High-Degree Heterogeneity: Federated Learning in Aerial and Space Networks0
Navigating the Future of Federated Recommendation Systems with Foundation Models0
Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning0
NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection0
Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation0
NebulaFL: Effective Asynchronous Federated Learning for JointCloud Computing0
NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients0
Neighborhood and Global Perturbations Supported SAM in Federated Learning: From Local Tweaks To Global Awareness0
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data0
Network Adaptive Federated Learning: Congestion and Lossy Compression0
Network Anomaly Detection Using Federated Learning and Transfer Learning0
Network Anomaly Detection Using Federated Learning0
Network EM Algorithm for Gaussian Mixture Model in Decentralized Federated Learning0
Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus0
Network Gradient Descent Algorithm for Decentralized Federated Learning0
Network Structures as an Attack Surface: Topology-Based Privacy Leakage in Federated Learning0
Neural Collapse based Deep Supervised Federated Learning for Signal Detection in OFDM Systems0
Neural Collapse Inspired Federated Learning with Non-iid Data0
Neural Tangent Kernel Empowered Federated Learning0
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA0
New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design0
New Insights on Unfolding and Fine-tuning Quantum Federated Learning0
New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning0
No Free Lunch Theorem for Security and Utility in Federated Learning0
Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks0
Noise-Robust and Resource-Efficient ADMM-based Federated Learning0
Non-Coherent Over-the-Air Decentralized Gradient Descent0
Non-convex composite federated learning with heterogeneous data0
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data0
Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive 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