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 2201–2225 of 6771 papers

Title	Date	Tasks	Status	Hype
Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption	Mar 17, 2024	Federated LearningPrivacy Preserving	CodeCode Available	1
Federated Transfer Learning with Differential Privacy	Mar 17, 2024	Federated Learningregression	—Unverified	0
Feature Aggregation with Latent Generative Replay for Federated Continual Learning of Socially Appropriate Robot Behaviours	Mar 16, 2024	Continual LearningCPU	—Unverified	0
Federated Learning based on Pruning and Recovery	Mar 16, 2024	Federated Learning	CodeCode Available	0
FedQNN: Federated Learning using Quantum Neural Networks	Mar 16, 2024	Federated LearningQuantum Machine Learning	—Unverified	0
FAGH: Accelerating Federated Learning with Approximated Global Hessian	Mar 16, 2024	Federated Learning	—Unverified	0
Enhancing IoT Security Against DDoS Attacks through Federated Learning	Mar 16, 2024	Dimensionality ReductionFederated Learning	—Unverified	0
Defense via Behavior Attestation against Attacks in Connected and Automated Vehicles based Federated Learning Systems	Mar 14, 2024	Federated Learning	—Unverified	0
Fairness-Aware Multi-Server Federated Learning Task Delegation over Wireless Networks	Mar 14, 2024	FairnessFederated Learning	—Unverified	0
Empowering Healthcare through Privacy-Preserving MRI Analysis	Mar 14, 2024	Federated LearningPrivacy Preserving	—Unverified	0
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models	Mar 14, 2024	Federated LearningQuantization	—Unverified	0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems	Mar 14, 2024	Anomaly DetectionExplainable artificial intelligence	—Unverified	0
Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios	Mar 14, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Learning from straggler clients in federated learning	Mar 14, 2024	Federated Learning	—Unverified	0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning	Mar 14, 2024	Federated LearningPersonalized Federated Learning	—Unverified	0
Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains	Mar 14, 2024	ClusteringFederated Learning	—Unverified	0
MGIC: A Multi-Label Gradient Inversion Attack based on Canny Edge Detection on Federated Learning	Mar 13, 2024	Distributed ComputingEdge Detection	—Unverified	0
AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in Federated Learning	Mar 13, 2024	Edge DetectionFederated Learning	—Unverified	0
Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics	Mar 13, 2024	Federated Learning	—Unverified	0
Federated Knowledge Graph Unlearning via Diffusion Model	Mar 13, 2024	Federated LearningKnowledge Graphs	—Unverified	0
Efficient Language Model Architectures for Differentially Private Federated Learning	Mar 12, 2024	Computational EfficiencyFederated Learning	—Unverified	0
Federated Learning of Socially Appropriate Agent Behaviours in Simulated Home Environments	Mar 12, 2024	Continual LearningFederated Learning	CodeCode Available	0
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning	Mar 11, 2024	Domain GeneralizationFederated Learning	—Unverified	0
UAV-Enabled Asynchronous Federated Learning	Mar 11, 2024	Federated Learning	—Unverified	0
Don't Forget What I did?: Assessing Client Contributions in Federated Learning	Mar 11, 2024	Data PoisoningFairness	—Unverified	0

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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