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 1626–1650 of 6771 papers

Title	Date	Tasks	Status
Privacy-preserving gradient-based fair federated learning	Jul 18, 2024	Collaborative FairnessFairness	—Unverified
Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus Algorithm	Jul 17, 2024	Federated Learning	CodeCode Available
A Framework for testing Federated Learning algorithms using an edge-like environment	Jul 17, 2024	Edge-computingFederated Learning	CodeCode Available
Individualized Federated Learning for Traffic Prediction with Error Driven Aggregation	Jul 17, 2024	Federated LearningPrediction	CodeCode Available
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering	Jul 17, 2024	Activity RecognitionClustering	—Unverified
Jigsaw Game: Federated Clustering	Jul 17, 2024	ClusteringFederated Learning	—Unverified
Proximity-based Self-Federated Learning	Jul 17, 2024	Edge-computingFederated Learning	—Unverified
Non-parametric regularization for class imbalance federated medical image classification	Jul 17, 2024	Federated Learningimage-classification	CodeCode Available
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems	Jul 17, 2024	Federated LearningStochastic Optimization	—Unverified
Comparing Federated Stochastic Gradient Descent and Federated Averaging for Predicting Hospital Length of Stay	Jul 17, 2024	Federated Learning	—Unverified
Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience	Jul 16, 2024	Federated Learning	—Unverified
Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communication	Jul 16, 2024	Anomaly DetectionFederated Learning	—Unverified
CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imaging	Jul 16, 2024	DiversityFederated Learning	—Unverified
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification	Jul 16, 2024	Federated Learningimage-classification	—Unverified
R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models	Jul 16, 2024	Federated LearningScheduling	—Unverified
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning	Jul 13, 2024	Data PoisoningFederated Learning	—Unverified
Harvesting Private Medical Images in Federated Learning Systems with Crafted Models	Jul 13, 2024	Federated LearningPrivacy Preserving	—Unverified
Overcoming Catastrophic Forgetting in Federated Class-Incremental Learning via Federated Global Twin Generator	Jul 13, 2024	class-incremental learningClass Incremental Learning	—Unverified
Combining Federated Learning and Control: A Survey	Jul 12, 2024	Decision MakingFederated Learning	—Unverified
Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses	Jul 12, 2024	Federated Learning	—Unverified
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning	Jul 12, 2024	Anomaly DetectionBackdoor Attack	—Unverified
Novel clustered federated learning based on local loss	Jul 12, 2024	ClusteringFederated Learning	CodeCode Available
Multi-Modal Dataset Creation for Federated Learning with DICOM Structured Reports	Jul 12, 2024	Data IntegrationFederated Learning	—Unverified
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization	Jul 12, 2024	Distributed OptimizationFederated Learning	—Unverified
FedVAE: Trajectory privacy preserving based on Federated Variational AutoEncoder	Jul 12, 2024	Federated LearningPrivacy Preserving	—Unverified

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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