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 2976–3000 of 6771 papers

Title	Date	Tasks	Status	Hype
Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey	Sep 19, 2023	Federated LearningMixed Reality	—Unverified	0
Toward efficient resource utilization at edge nodes in federated learning	Sep 19, 2023	CPUFederated Learning	—Unverified	0
FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Prediction	Sep 19, 2023	Federated LearningPrediction	—Unverified	0
Towards Energy-Aware Federated Traffic Prediction for Cellular Networks	Sep 19, 2023	Federated LearningPrediction	CodeCode Available	1
Communication-Efficient Federated Learning via Regularized Sparse Random Networks	Sep 19, 2023	Federated Learning	—Unverified	0
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning	Sep 19, 2023	Computational EfficiencyFederated Learning	—Unverified	0
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data	Sep 18, 2023	Federated LearningScheduling	—Unverified	0
A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning	Sep 18, 2023	Federated LearningVertical Federated Learning	—Unverified	0
Federated Learning in Temporal Heterogeneity	Sep 17, 2023	Federated Learning	—Unverified	0
User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks	Sep 17, 2023	Combinatorial OptimizationCPU	—Unverified	0
UNIDEAL: Curriculum Knowledge Distillation Federated Learning	Sep 16, 2023	Federated LearningKnowledge Distillation	—Unverified	0
XFedHunter: An Explainable Federated Learning Framework for Advanced Persistent Threat Detection in SDN	Sep 15, 2023	Federated LearningGraph Neural Network	—Unverified	0
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning	Sep 15, 2023	Data AugmentationDisentanglement	CodeCode Available	1
FedJudge: Federated Legal Large Language Model	Sep 15, 2023	Continual LearningFederated Learning	CodeCode Available	1
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems	Sep 14, 2023	Federated LearningRecommendation Systems	—Unverified	0
Communication Efficient Private Federated Learning Using Dithering	Sep 14, 2023	Federated LearningQuantization	CodeCode Available	0
Client-side Gradient Inversion Against Federated Learning from Poisoning	Sep 14, 2023	Federated Learning	—Unverified	0
Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments	Sep 13, 2023	Autonomous VehiclesFederated Learning	CodeCode Available	0
Learning From Drift: Federated Learning on Non-IID Data via Drift Regularization	Sep 13, 2023	Federated Learning	—Unverified	0
Federated PAC-Bayesian Learning on Non-IID data	Sep 13, 2023	Federated Learning	—Unverified	0
FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization	Sep 13, 2023	Federated Learning	CodeCode Available	1
Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonization	Sep 13, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Mitigating Group Bias in Federated Learning for Heterogeneous Devices	Sep 13, 2023	Decision MakingEmotion Recognition	—Unverified	0
Fingerprint Attack: Client De-Anonymization in Federated Learning	Sep 12, 2023	ClusteringFederated Learning	CodeCode Available	0
Federated Learning for Large-Scale Scene Modeling with Neural Radiance Fields	Sep 12, 2023	Federated LearningLifelong learning	—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