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 27012750 of 6771 papers

TitleStatusHype
Decentralized Federated Learning Over Imperfect Communication Channels0
Automatic Tuning of Federated Learning Hyper-Parameters from System Perspective0
A High Energy-Efficiency Multi-core Neuromorphic Architecture for Deep SNN Training0
Decentralized Federated Learning on the Edge over Wireless Mesh Networks0
Decentralized federated learning of deep neural networks on non-iid data0
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis0
Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networks0
A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs0
Adaptive Federated Learning Over the Air0
Accessible Gesture-Driven Augmented Reality Interaction System0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey0
Decentralized Federated Learning: A Survey on Security and Privacy0
Decentralized Federated Learning: A Survey and Perspective0
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning0
A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach0
Decentralized Federated Domain Generalization with Style Sharing: A Formal Modeling and Convergence Analysis0
Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach0
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models0
Decentralized EM to Learn Gaussian Mixtures from Datasets Distributed by Features0
A Graph Federated Architecture with Privacy Preserving Learning0
Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Sampling0
Decentralized Directed Collaboration for Personalized Federated Learning0
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence0
Decentralized digital twins of complex dynamical systems0
Decentralized Differentially Private Segmentation with PATE0
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning0
AGRAMPLIFIER: Defending Federated Learning Against Poisoning Attacks Through Local Update Amplification0
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness0
Decentralized Blockchain-based Robust Multi-agent Multi-armed Bandit0
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo0
Decentralized Bayesian Learning over Graphs0
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation0
Agnostic Personalized Federated Learning with Kernel Factorization0
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things0
Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space0
Decentralised Traffic Incident Detection via Network Lasso0
AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving0
Distributed Machine Learning with Sparse Heterogeneous Data0
Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation0
Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning0
Decentralised and collaborative machine learning framework for IoT0
Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review0
DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models0
AGIC: Approximate Gradient Inversion Attack on Federated Learning0
Decaf: Data Distribution Decompose Attack against Federated Learning0
Debiasing Federated Learning with Correlated Client Participation0
DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning0
A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off in Trustworthy Federated Learning0
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy0
DEAL: Decremental Energy-Aware Learning in a Federated System0
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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