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 17011750 of 6771 papers

TitleStatusHype
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement LearningCode0
Federated Binary Matrix Factorization using Proximal Optimization0
A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated LearningCode0
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation0
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis0
Personalized Interpretation on Federated Learning: A Virtual Concepts approach0
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
Personalized Federated Continual Learning via Multi-granularity PromptCode1
Towards Personalized Federated Multi-Scenario Multi-Task Recommendation0
FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient Federated Learning0
FedMLP: Federated Multi-Label Medical Image Classification under Task HeterogeneityCode1
Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design0
FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization0
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling0
Enhancing Federated Learning with Adaptive Differential Privacy and Priority-Based Aggregation0
QBI: Quantile-Based Bias Initialization for Efficient Private Data Reconstruction in Federated LearningCode0
Maze Discovery using Multiple Robots via Federated Learning0
Navigating High-Degree Heterogeneity: Federated Learning in Aerial and Space Networks0
Entity Augmentation for Efficient Classification of Vertically Partitioned Data with Limited Overlap0
FedBiOT: LLM Local Fine-tuning in Federated Learning without Full ModelCode2
Task-Agnostic Federated Learning0
Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees0
Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning0
Achieving Fairness Across Local and Global Models in Federated Learning0
Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework0
Personalized federated learning based on feature fusion0
Federated Transfer Learning Aided Interference Classification in GNSS Signals0
Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning0
Privacy Preserving Machine Learning for Electronic Health Records using Federated Learning and Differential Privacy0
Semi-Variance Reduction for Fair Federated Learning0
Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things0
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning0
Federated Adversarial Learning for Robust Autonomous Landing Runway Detection0
Supersonic OT: Fast Unconditionally Secure Oblivious Transfer0
Privacy Preserved Blood Glucose Level Cross-Prediction: An Asynchronous Decentralized Federated Learning ApproachCode0
Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training0
Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach0
Rate-Splitting Multiple Access for Overloaded Multi-group Multicast: A First Experimental Study0
Safely Learning with Private Data: A Federated Learning Framework for Large Language ModelCode1
Tempora-Fusion: Time-Lock Puzzle with Efficient Verifiable Homomorphic Linear Combination0
FLoCoRA: Federated learning compression with low-rank adaptationCode0
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated LearningCode0
Communication-efficient Vertical Federated Learning via Compressed Error FeedbackCode0
Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization0
CollaFuse: Collaborative Diffusion ModelsCode0
DRACO: Decentralized Asynchronous Federated Learning over Row-Stochastic Wireless Networks0
Bayes' capacity as a measure for reconstruction attacks in federated learning0
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments0
SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning0
Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing0
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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