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 1826–1850 of 6771 papers

Title	Date	Tasks	Status
Efficient and Private Federated Learning with Partially Trainable Networks	Oct 6, 2021	Federated Learning	—Unverified
Efficient and Reliable Overlay Networks for Decentralized Federated Learning	Dec 12, 2021	Federated LearningGeneralization Bounds	—Unverified
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning	Nov 13, 2021	Anomaly DetectionFederated Learning	—Unverified
Efficient and Secure Federated Learning for Financial Applications	Mar 15, 2023	Federated Learning	—Unverified
Efficient Asynchronous Federated Learning with Sparsification and Quantization	Dec 23, 2023	Federated LearningQuantization	—Unverified
Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost	Dec 8, 2021	Federated LearningQuantization	—Unverified
Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning	Feb 17, 2023	Federated LearningPrivacy Preserving	—Unverified
Efficient Client Contribution Evaluation for Horizontal Federated Learning	Feb 26, 2021	Federated Learning	—Unverified
Efficient Client Selection in Federated Learning	Jan 25, 2025	Anomaly DetectionFederated Learning	—Unverified
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach	Oct 29, 2023	Federated LearningPersonalized Federated Learning	—Unverified
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems	Jan 22, 2024	Federated Learning	—Unverified
Efficient Conformal Prediction under Data Heterogeneity	Dec 25, 2023	Conformal PredictionFederated Learning	—Unverified
Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning	Sep 21, 2023	Federated Learning	—Unverified
Efficient Cross-Domain Federated Learning by MixStyle Approximation	Dec 12, 2023	Federated LearningPrivacy Preserving	—Unverified
Efficient Data Distribution Estimation for Accelerated Federated Learning	Jun 3, 2024	Federated LearningPrivacy Preserving	—Unverified
Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach	Apr 23, 2025	Data ValuationFederated Learning	—Unverified
Efficient Deployment of Large Language Models on Resource-constrained Devices	Jan 5, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified
Efficient Device Scheduling with Multi-Job Federated Learning	Dec 11, 2021	Bayesian OptimizationFairness	—Unverified
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors	Dec 13, 2021	Edge-computingFederated Learning	—Unverified
Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning	Apr 9, 2022	Federated Learning	—Unverified
Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices	Nov 12, 2024	Federated Learning	—Unverified
Byzantine-resilient Federated Learning Employing Normalized Gradients on Non-IID Datasets	Aug 18, 2024	Federated Learning	—Unverified
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis	Mar 9, 2023	Contrastive LearningFederated Learning	—Unverified
Efficient Federated Learning for AIoT Applications Using Knowledge Distillation	Nov 29, 2021	Federated LearningKnowledge Distillation	—Unverified
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation	Aug 15, 2022	Federated Learning	—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