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 61016150 of 6771 papers

TitleStatusHype
Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space0
Decentralized Bayesian Learning over Graphs0
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo0
Decentralized Blockchain-based Robust Multi-agent Multi-armed Bandit0
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness0
Decentralized Differentially Private Segmentation with PATE0
Decentralized digital twins of complex dynamical systems0
Decentralized Directed Collaboration for Personalized Federated Learning0
Decentralized EM to Learn Gaussian Mixtures from Datasets Distributed by Features0
Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach0
Decentralized Federated Domain Generalization with Style Sharing: A Formal Modeling and Convergence Analysis0
Decentralized Federated Learning: A Survey and Perspective0
Decentralized Federated Learning: A Survey on Security and Privacy0
Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networks0
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis0
Decentralized federated learning of deep neural networks on non-iid data0
Decentralized Federated Learning on the Edge over Wireless Mesh Networks0
Decentralized Federated Learning Over Imperfect Communication Channels0
Decentralized Federated Learning Preserves Model and Data Privacy0
Decentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization0
Decentralized Federated Learning via Mutual Knowledge Transfer0
Decentralized Federated Learning with Gradient Tracking over Time-Varying Directed Networks0
Decentralized Federated Learning with Model Caching on Mobile Agents0
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control0
BlockFUL: Enabling Unlearning in Blockchained Federated Learning0
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks0
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI0
Decentralized Health Intelligence Network (DHIN)0
Decentralized Intelligence Network (DIN)0
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities0
Decentralized Low-Rank Fine-Tuning of Large Language Models0
Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum0
Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection0
Decentralized Personalized Federated Learning0
Decentralized Personalized Federated Learning for Min-Max Problems0
Decentralized Personalized Online Federated Learning0
Federated Continual Learning for Socially Aware Robotics0
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models0
Decoding FL Defenses: Systemization, Pitfalls, and Remedies0
Decoupled Federated Learning for ASR with Non-IID Data0
Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics0
Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data0
DEED: A General Quantization Scheme for Communication Efficiency in Bits0
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach0
Deep Convolutional Neural Networks for Short-Term Multi-Energy Demand Prediction of Integrated Energy Systems0
Deep Efficient Private Neighbor Generation for Subgraph Federated Learning0
Deep Equilibrium Models Meet Federated Learning0
Deep Federated Anomaly Detection for Multivariate Time Series Data0
Deep Hierarchy Quantization Compression algorithm based on Dynamic Sampling0
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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