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 501–550 of 6771 papers

TitleStatusHype
Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning—0
Redefining non-IID Data in Federated Learning for Computer Vision Tasks: Migrating from Labels to Embeddings for Task-Specific Data DistributionsCode0
Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation—0
Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-MismatchCode1
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems—0
Federated Learning with Domain Shift Eraser—0
Fed-Joint: Joint Modeling of Nonlinear Degradation Signals and Failure Events for Remaining Useful Life Prediction using Federated Learning—0
Federated Continual Instruction Tuning—0
PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated LearningCode0
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation—0
FedVSR: Towards Model-Agnostic Federated Learning in Video Super-ResolutionCode1
Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning—0
FedGAI: Federated Style Learning with Cloud-Edge Collaboration for Generative AI in Fashion Design—0
Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning—0
XAI-Driven Client Selection for Federated Learning in Scalable 6G Network Slicing—0
A State Alignment-Centric Approach to Federated System Identification: The FedAlign Framework—0
FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning—0
PA-CFL: Privacy-Adaptive Clustered Federated Learning for Transformer-Based Sales Forecasting on Heterogeneous Retail Data—0
A Survey on Federated Fine-tuning of Large Language ModelsCode2
Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning—0
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective—0
Federated Learning for Secure and Efficient Device Activity Detection in mMTC Networks—0
Layer-wise Update Aggregation with Recycling for Communication-Efficient Federated Learning—0
Performance Analysis of Decentralized Federated Learning Deployments—0
Enabling Weak Client Participation via On-device Knowledge Distillation in Heterogenous Federated Learning—0
PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications—0
Federated Koopman-Reservoir Learning for Large-Scale Multivariate Time-Series Anomaly Detection—0
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
PluralLLM: Pluralistic Alignment in LLMs via Federated Learning—0
FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis—0
A Multi-Modal Federated Learning Framework for Remote Sensing Image Classification—0
dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis—0
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion AttacksCode1
One-Shot Federated Unsupervised Domain Adaptation with Scaled Entropy Attention and Multi-Source Smoothed Pseudo Labeling—0
Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models—0
Privacy-Preserved Automated Scoring using Federated Learning for Educational ResearchCode0
A Comprehensive Review on Understanding the Decentralized and Collaborative Approach in Machine Learning—0
Robust Asymmetric Heterogeneous Federated Learning with Corrupted ClientsCode0
Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing—0
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning—0
Technical Insights and Legal Considerations for Advancing Federated Learning in BioinformaticsCode0
Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning—0
Mitigating Membership Inference Vulnerability in Personalized Federated Learning—0
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates—0
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State MatchingCode1
Drift-Aware Federated Learning: A Causal Perspective—0
Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data—0
Scaling Probabilistic Circuits via Data PartitioningCode0
Smoothing ADMM for Non-convex and Non-smooth Hierarchical Federated Learning—0
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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