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 2951–2975 of 6771 papers

Title	Date	Tasks	Status	Hype
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations	Sep 27, 2023	Federated LearningPersonalized Federated Learning	—Unverified	0
Federated Deep Equilibrium Learning: Harnessing Compact Global Representations to Enhance Personalization	Sep 27, 2023	Federated LearningInformation Retrieval	—Unverified	0
PLMM: Personal Large Language Models on Mobile Devices	Sep 26, 2023	Federated Learning	—Unverified	0
Markov Chain Mirror Descent On Data Federation	Sep 26, 2023	Federated LearningStochastic Optimization	—Unverified	0
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler	Sep 26, 2023	Federated Learning	CodeCode Available	1
Federated Learning Under Restricted User Availability	Sep 25, 2023	Federated Learning	—Unverified	0
REPA: Client Clustering without Training and Data Labels for Improved Federated Learning in Non-IID Settings	Sep 25, 2023	ClusteringFederated Learning	—Unverified	0
REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices	Sep 24, 2023	Federated Learning	—Unverified	0
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning	Sep 24, 2023	Data-free Knowledge DistillationDiversity	—Unverified	0
Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness	Sep 24, 2023	Computational EfficiencyFederated Learning	CodeCode Available	0
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous Driving	Sep 23, 2023	Autonomous DrivingDomain Generalization	CodeCode Available	1
Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASR	Sep 22, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients	Sep 22, 2023	Federated LearningLoad Forecasting	—Unverified	0
Expressive variational quantum circuits provide inherent privacy in federated learning	Sep 22, 2023	Federated LearningQuantum Machine Learning	—Unverified	0
Improving Machine Learning Robustness via Adversarial Training	Sep 22, 2023	Federated Learning	—Unverified	0
Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training	Sep 21, 2023	backdoor defenseDistributed Computing	CodeCode Available	1
Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training	Sep 21, 2023	backdoor defenseDistributed Computing	CodeCode Available	1
Enabling Quartile-based Estimated-Mean Gradient Aggregation As Baseline for Federated Image Classifications	Sep 21, 2023	Federated Learning	—Unverified	0
Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning	Sep 21, 2023	Federated Learning	—Unverified	0
Likelihood-based Sensor Calibration using Affine Transformation	Sep 20, 2023	Federated Learning	—Unverified	0
Federated Learning with Neural Graphical Models	Sep 20, 2023	Federated Learning	—Unverified	0
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration	Sep 20, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Preconditioned Federated Learning	Sep 20, 2023	Federated LearningPrivacy Preserving	—Unverified	0
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems	Sep 20, 2023	Federated LearningObject Recognition	—Unverified	0
Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion	Sep 20, 2023	ClusteringFederated 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