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

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Showing 1301–1350 of 6771 papers

TitleStatusHype
Technical Report: Aggregation on Learnable Manifolds for Asynchronous Federated Optimization—0
Advanced Relay-Based Collaborative Framework for Optimizing Synchronization in Split Federated Learning over Wireless Networks—0
Trading-off Accuracy and Communication Cost in Federated Learning—0
Zero-Knowledge Federated Learning: A New Trustworthy and Privacy-Preserving Distributed Learning Paradigm—0
Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning—0
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation—0
Federated Continual Instruction Tuning—0
PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated LearningCode0
Federated Learning with Domain Shift Eraser—0
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems—0
Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation—0
Redefining non-IID Data in Federated Learning for Computer Vision Tasks: Migrating from Labels to Embeddings for Task-Specific Data DistributionsCode0
Fed-Joint: Joint Modeling of Nonlinear Degradation Signals and Failure Events for Remaining Useful Life Prediction using Federated Learning—0
Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning—0
Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning—0
FedGAI: Federated Style Learning with Cloud-Edge Collaboration for Generative AI in Fashion Design—0
XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing—0
PA-CFL: Privacy-Adaptive Clustered Federated Learning for Transformer-Based Sales Forecasting on Heterogeneous Retail Data—0
FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning—0
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective—0
A State Alignment-Centric Approach to Federated System Identification: The FedAlign Framework—0
Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning—0
Federated Koopman-Reservoir Learning for Large-Scale Multivariate Time-Series Anomaly Detection—0
PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications—0
Layer-wise Update Aggregation with Recycling for Communication-Efficient Federated Learning—0
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration—0
Federated Learning for Secure and Efficient Device Activity Detection in mMTC Networks—0
Performance Analysis of Decentralized Federated Learning Deployments—0
Enabling Weak Client Participation via On-device Knowledge Distillation in Heterogenous Federated Learning—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models—0
A Multi-Modal Federated Learning Framework for Remote Sensing Image Classification—0
One-Shot Federated Unsupervised Domain Adaptation with Scaled Entropy Attention and Multi-Source Smoothed Pseudo Labeling—0
dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis—0
FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis—0
PluralLLM: Pluralistic Alignment in LLMs via Federated Learning—0
Privacy-Preserved Automated Scoring using Federated Learning for Educational ResearchCode0
Robust Asymmetric Heterogeneous Federated Learning with Corrupted ClientsCode0
Technical Insights and Legal Considerations for Advancing Federated Learning in BioinformaticsCode0
Mitigating Membership Inference Vulnerability in Personalized Federated Learning—0
Drift-Aware Federated Learning: A Causal Perspective—0
Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing—0
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates—0
Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning—0
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning—0
A Comprehensive Review on Understanding the Decentralized and Collaborative Approach in Machine Learning—0
Smoothing ADMM for Non-convex and Non-smooth Hierarchical Federated Learning—0
Scaling Probabilistic Circuits via Data PartitioningCode0
Extra Clients at No Extra Cost: Overcome Data Heterogeneity in Federated Learning with Filter Decomposition—0
Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data—0
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All datasetsCityscapes heterogeneousLandmarks-User-160kCIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SiloBN + ASAMmIoU49.75—Unverified
2SiloBN + SAMmIoU49.1—Unverified
3SiloBNmIoU45.96—Unverified
4FedSAM + SWAmIoU43.42—Unverified
5FedASAM + SWAmIoU43.02—Unverified
6FedAvg + SWAmIoU42.48—Unverified
7FedASAMmIoU42.27—Unverified
8FedSAMmIoU41.22—Unverified
9FedAvgmIoU38.65—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAAcc@1-1262Clients68.32—Unverified
2FedSAM + SWAAcc@1-1262Clients68.12—Unverified
3FedAvg + SWAAcc@1-1262Clients67.52—Unverified
4FedASAMAcc@1-1262Clients64.23—Unverified
5FedSAMAcc@1-1262Clients63.72—Unverified
6FedAvgAcc@1-1262Clients61.91—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.64—Unverified
2FedASAMACC@1-100Clients39.76—Unverified
3FedSAM + SWAACC@1-100Clients39.51—Unverified
4FedSAMACC@1-100Clients36.93—Unverified
5FedAvgACC@1-100Clients36.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients41.62—Unverified
2FedASAMACC@1-100Clients40.81—Unverified
3FedSAM + SWAACC@1-100Clients39.24—Unverified
4FedAvgACC@1-100Clients38.59—Unverified
5FedSAMACC@1-100Clients38.56—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.72—Unverified
2FedSAM + SWAACC@1-100Clients46.76—Unverified
3FedASAMACC@1-100Clients46.58—Unverified
4FedSAMACC@1-100Clients44.84—Unverified
5FedAvgACC@1-100Clients41.27—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients48.27—Unverified
2FedASAMACC@1-100Clients47.78—Unverified
3FedSAM + SWAACC@1-100Clients46.47—Unverified
4FedSAMACC@1-100Clients46.05—Unverified
5FedAvgACC@1-100Clients42.17—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients49.17—Unverified
2FedSAM + SWAACC@1-100Clients47.96—Unverified
3FedASAMACC@1-100Clients45.61—Unverified
4FedSAMACC@1-100Clients44.73—Unverified
5FedAvgACC@1-100Clients40.43—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAM + SWAACC@1-100Clients42.01—Unverified
2FedSAM + SWAACC@1-100Clients39.3—Unverified
3FedASAMACC@1-100Clients36.04—Unverified
4FedSAMACC@1-100Clients31.04—Unverified
5FedAvgACC@1-100Clients30.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.97—Unverified
2FedASAM + SWAACC@1-100Clients54.79—Unverified
3FedSAM + SWAACC@1-100Clients53.67—Unverified
4FedSAMACC@1-100Clients53.39—Unverified
5FedAvgACC@1-100Clients50.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.5—Unverified
2FedSAM + SWAACC@1-100Clients54.36—Unverified
3FedASAM + SWAACC@1-100Clients54.1—Unverified
4FedSAMACC@1-100Clients53.97—Unverified
5FedAvgACC@1-100Clients50.66—Unverified
#ModelMetricClaimedVerifiedStatus
1FedASAMACC@1-100Clients54.81—Unverified
2FedSAMACC@1-100Clients54.01—Unverified
3FedSAM + SWAACC@1-100Clients53.9—Unverified
4FedASAM + SWAACC@1-100Clients53.86—Unverified
5FedAvgACC@1-100Clients49.92—Unverified
#ModelMetricClaimedVerifiedStatus
1AdaBestAverage Top-1 Accuracy56.2—Unverified