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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 6401–6425 of 6771 papers

Title	Date	Tasks	Status
Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEs	Jul 3, 2025	Federated Learning	CodeCode Available
A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation	Jun 14, 2023	DiagnosticDomain Adaptation	CodeCode Available
REFOL: Resource-Efficient Federated Online Learning for Traffic Flow Forecasting	Nov 21, 2024	Federated Learning	CodeCode Available
3FM: Multi-modal Meta-learning for Federated Tasks	Dec 15, 2023	Federated LearningMeta-Learning	CodeCode Available
Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation	Jan 30, 2023	Federated LearningPosition	CodeCode Available
Multi-Level Additive Modeling for Structured Non-IID Federated Learning	May 26, 2024	Additive modelsFederated Learning	CodeCode Available
Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples via Least-Squares Solution	Oct 26, 2022	Data IntegrationFederated Learning	CodeCode Available
Federated Learning Client Pruning for Noisy Labels	Nov 11, 2024	Federated Learning	CodeCode Available
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling	Sep 7, 2021	Federated Learning	CodeCode Available
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems	Jun 13, 2023	Edge-computingFederated Learning	CodeCode Available
Covariances for Free: Exploiting Mean Distributions for Federated Learning with Pre-Trained Models	Dec 18, 2024	Federated Learning	CodeCode Available
Regularizing and Aggregating Clients with Class Distribution for Personalized Federated Learning	Jun 12, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available
Gradient Coreset for Federated Learning	Jan 13, 2024	Federated Learning	CodeCode Available
SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data	Jul 29, 2023	ClusteringFederated Learning	CodeCode Available
A Federated Random Forest Solution for Secure Distributed Machine Learning	May 12, 2025	Federated LearningIncremental Learning	CodeCode Available
Federated Learning based on Pruning and Recovery	Mar 16, 2024	Federated Learning	CodeCode Available
Adaptive Gradient-Based Meta-Learning Methods	Jun 6, 2019	Federated LearningFew-Shot Learning	CodeCode Available
CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT Environment	Jul 22, 2023	Federated Learning	CodeCode Available
Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities	Jan 7, 2024	Federated LearningMedical Image Analysis	CodeCode Available
Supernet Training for Federated Image Classification under System Heterogeneity	Jun 3, 2022	ClassificationFederated Learning	CodeCode Available
Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning	Jun 4, 2025	Federated Learningparameter-efficient fine-tuning	CodeCode Available
Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability	Sep 26, 2024	Federated Learning	CodeCode Available
Federated Learning Based Multilingual Emoji Prediction In Clean and Attack Scenarios	Mar 30, 2023	Federated LearningPrediction	CodeCode Available
Gradient Leakage Defense with Key-Lock Module for Federated Learning	May 6, 2023	Federated LearningPrivacy Preserving	CodeCode Available
Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data	Apr 26, 2024	ECG ClassificationFederated Learning	CodeCode Available

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All datasets Cityscapes heterogeneous Landmarks-User-160k CIFAR-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

#	Model	Metric	Claimed	Verified	Status
1	SiloBN + ASAM	mIoU	49.75	—	Unverified
2	SiloBN + SAM	mIoU	49.1	—	Unverified
3	SiloBN	mIoU	45.96	—	Unverified
4	FedSAM + SWA	mIoU	43.42	—	Unverified
5	FedASAM + SWA	mIoU	43.02	—	Unverified
6	FedAvg + SWA	mIoU	42.48	—	Unverified
7	FedASAM	mIoU	42.27	—	Unverified
8	FedSAM	mIoU	41.22	—	Unverified
9	FedAvg	mIoU	38.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	Acc@1-1262Clients	68.32	—	Unverified
2	FedSAM + SWA	Acc@1-1262Clients	68.12	—	Unverified
3	FedAvg + SWA	Acc@1-1262Clients	67.52	—	Unverified
4	FedASAM	Acc@1-1262Clients	64.23	—	Unverified
5	FedSAM	Acc@1-1262Clients	63.72	—	Unverified
6	FedAvg	Acc@1-1262Clients	61.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	42.64	—	Unverified
2	FedASAM	ACC@1-100Clients	39.76	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	39.51	—	Unverified
4	FedSAM	ACC@1-100Clients	36.93	—	Unverified
5	FedAvg	ACC@1-100Clients	36.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	41.62	—	Unverified
2	FedASAM	ACC@1-100Clients	40.81	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	39.24	—	Unverified
4	FedAvg	ACC@1-100Clients	38.59	—	Unverified
5	FedSAM	ACC@1-100Clients	38.56	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	48.72	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	46.76	—	Unverified
3	FedASAM	ACC@1-100Clients	46.58	—	Unverified
4	FedSAM	ACC@1-100Clients	44.84	—	Unverified
5	FedAvg	ACC@1-100Clients	41.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	48.27	—	Unverified
2	FedASAM	ACC@1-100Clients	47.78	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	46.47	—	Unverified
4	FedSAM	ACC@1-100Clients	46.05	—	Unverified
5	FedAvg	ACC@1-100Clients	42.17	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	49.17	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	47.96	—	Unverified
3	FedASAM	ACC@1-100Clients	45.61	—	Unverified
4	FedSAM	ACC@1-100Clients	44.73	—	Unverified
5	FedAvg	ACC@1-100Clients	40.43	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	42.01	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	39.3	—	Unverified
3	FedASAM	ACC@1-100Clients	36.04	—	Unverified
4	FedSAM	ACC@1-100Clients	31.04	—	Unverified
5	FedAvg	ACC@1-100Clients	30.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.97	—	Unverified
2	FedASAM + SWA	ACC@1-100Clients	54.79	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	53.67	—	Unverified
4	FedSAM	ACC@1-100Clients	53.39	—	Unverified
5	FedAvg	ACC@1-100Clients	50.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.5	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	54.36	—	Unverified
3	FedASAM + SWA	ACC@1-100Clients	54.1	—	Unverified
4	FedSAM	ACC@1-100Clients	53.97	—	Unverified
5	FedAvg	ACC@1-100Clients	50.66	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.81	—	Unverified
2	FedSAM	ACC@1-100Clients	54.01	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	53.9	—	Unverified
4	FedASAM + SWA	ACC@1-100Clients	53.86	—	Unverified
5	FedAvg	ACC@1-100Clients	49.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	AdaBest	Average Top-1 Accuracy	56.2	—	Unverified