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 60516100 of 6771 papers

TitleStatusHype
D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data0
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning0
DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology0
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning0
DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLT0
Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning0
DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information0
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization0
Data-Agnostic Model Poisoning against Federated Learning: A Graph Autoencoder Approach0
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey0
Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies0
Data Assetization via Resources-decoupled Federated Learning0
Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings0
Data, Competition, and Digital Platforms0
Data Distribution Shifts in (Industrial) Federated Learning as a Privacy Issue0
Data-Driven Breakthroughs and Future Directions in AI Infrastructure: A Comprehensive Review0
Data-driven geophysics: from dictionary learning to deep learning0
Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning0
Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation0
Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis0
Data-Free Evaluation of User Contributions in Federated Learning0
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory0
Data-Heterogeneous Hierarchical Federated Learning with Mobility0
Data Obfuscation through Latent Space Projection (LSP) for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection0
Data Overvaluation Attack and Truthful Data Valuation in Federated Learning0
Data Poisoning Attacks on Federated Machine Learning0
Data privacy protection in microscopic image analysis for material data mining0
Data Quality Control in Federated Instruction-tuning of Large Language Models0
Data Reconstruction Attacks and Defenses: A Systematic Evaluation0
Data Selection for Efficient Model Update in Federated Learning0
Overcoming Noisy and Irrelevant Data in Federated Learning0
Dataset Distillation-based Hybrid Federated Learning on Non-IID Data0
Data Similarity-Based One-Shot Clustering for Multi-Task Hierarchical Federated Learning0
Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method0
Data value estimation on private gradients0
DBFed: Debiasing Federated Learning Framework based on Domain-Independent0
DCFL: Non-IID awareness Data Condensation aided Federated Learning0
D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning0
DEAL: Decremental Energy-Aware Learning in a Federated System0
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy0
DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning0
Debiasing Federated Learning with Correlated Client Participation0
Decaf: Data Distribution Decompose Attack against Federated Learning0
DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models0
Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review0
Decentralised and collaborative machine learning framework for IoT0
Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip Parallel SGD for Collaborative Learning0
Distributed Machine Learning with Sparse Heterogeneous Data0
Decentralised Traffic Incident Detection via Network Lasso0
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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