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 25012550 of 6771 papers

TitleStatusHype
Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced ModalitiesCode0
Distributed client selection with multi-objective in federated learning assisted Internet of Vehicles0
Convergence Rate Maximization for Split Learning-based Control of EMG Prosthetic Devices0
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated LearningCode2
FedNS: A Fast Sketching Newton-Type Algorithm for Federated LearningCode0
Federated Learning for distribution skewed data using sample weights0
Predicting Traffic Flow with Federated Learning and Graph Neural with Asynchronous Computations Network0
Fairness-Aware Job Scheduling for Multi-Job Federated Learning0
Adaptive Differential Privacy in Federated Learning: A Priority-Based Approach0
Exploring Vacant Classes in Label-Skewed Federated LearningCode0
PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental LearningCode1
Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing0
Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework0
The Internet of Things in the Era of Generative AI: Vision and Challenges0
Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneity0
FedQV: Leveraging Quadratic Voting in Federated LearningCode0
PPBFL: A Privacy Protected Blockchain-based Federated Learning Model0
FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning0
Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space0
Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping0
Device-Wise Federated Network PruningCode0
Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning0
FedHCA2: Towards Hetero-Client Federated Multi-Task LearningCode1
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce DataCode0
Federated Class-Incremental Learning with New-Class Augmented Self-DistillationCode1
Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDM0
A review on different techniques used to combat the non-IID and heterogeneous nature of data in FL0
Facebook Report on Privacy of fNIRS data0
Balanced Multi-modal Federated Learning via Cross-Modal Infiltration0
Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality SelectionCode0
Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning0
LEFL: Low Entropy Client Sampling in Federated LearningCode0
Replica Tree-based Federated Learning using Limited DataCode0
Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solution0
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning0
A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning0
Federated Continual Learning via Knowledge Fusion: A Survey0
Fault Tolerant Serverless VFL Over Dynamic Device Environment0
Harnessing the Power of Federated Learning in Federated Contextual BanditsCode0
Smuche: Scalar-Multiplicative Caching in Homomorphic Encryption0
FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Models0
Federated Hyperdimensional Computing0
Efficient Conformal Prediction under Data Heterogeneity0
Federated learning-outcome prediction with multi-layer privacy protection0
An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms0
Deep Convolutional Neural Networks for Short-Term Multi-Energy Demand Prediction of Integrated Energy Systems0
FedDMF: Privacy-Preserving User Attribute Prediction using Deep Matrix Factorization0
User Consented Federated Recommender System Against Personalized Attribute Inference AttackCode0
Efficient Asynchronous Federated Learning with Sparsification and Quantization0
Show:102550
← PrevPage 51 of 136Next →

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