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 26512700 of 6771 papers

TitleStatusHype
Information-Geometric Barycenters for Bayesian Federated Learning0
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach0
DEED: A General Quantization Scheme for Communication Efficiency in Bits0
B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction0
AI-Based Crypto Tokens: The Illusion of Decentralized AI?0
Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data0
Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics0
Decoupled Federated Learning for ASR with Non-IID Data0
A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity0
AI Approaches in Processing and Using Data in Personalized Medicine0
Adaptive Federated Minimax Optimization with Lower Complexities0
A Bayesian Federated Learning Framework with Online Laplace Approximation0
Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application0
Decoding FL Defenses: Systemization, Pitfalls, and Remedies0
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models0
A Web-Based Solution for Federated Learning with LLM-Based Automation0
Federated Continual Learning for Socially Aware Robotics0
Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data0
Decentralized Personalized Online Federated Learning0
Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations0
A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments0
Decentralized Personalized Federated Learning for Min-Max Problems0
Decentralized Personalized Federated Learning0
A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS0
Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection0
A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring0
A Hybrid Federated Kernel Regularized Least Squares Algorithm0
Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum0
Decentralized Low-Rank Fine-Tuning of Large Language Models0
A Vertical Federated Learning Framework for Graph Convolutional Network0
Decentralized Local Stochastic Extra-Gradient for Variational Inequalities0
Decentralized Intelligence Network (DIN)0
A Vertical Federated Learning Framework for Horizontally Partitioned Labels0
Large-Scale Secure XGB for Vertical Federated Learning0
Adaptive Federated Learning via New Entropy Approach0
Decentralized Health Intelligence Network (DHIN)0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI0
Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation0
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks0
AVDDPG: Federated reinforcement learning applied to autonomous platoon control0
BlockFUL: Enabling Unlearning in Blockchained Federated Learning0
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control0
Decentralized Federated Learning with Model Caching on Mobile Agents0
Decentralized Federated Learning with Gradient Tracking over Time-Varying Directed Networks0
Auxo: Efficient Federated Learning via Scalable Client Clustering0
A Huber Loss Minimization Approach to Byzantine Robust Federated Learning0
Decentralized Federated Learning via Mutual Knowledge Transfer0
Decentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization0
AutoRank: MCDA Based Rank Personalization for LoRA-Enabled Distributed Learning0
Decentralized Federated Learning Preserves Model and Data Privacy0
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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