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 1226–1250 of 6771 papers

Title	Date	Tasks	Status	Hype
Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer	Oct 19, 2024	Federated LearningPersonalized Federated Learning	—Unverified	0
FedSpaLLM: Federated Pruning of Large Language Models	Oct 18, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning	Oct 18, 2024	Decision MakingFederated Learning	—Unverified	0
Comparative Evaluation of Clustered Federated Learning Methods	Oct 18, 2024	Federated Learningimage-classification	CodeCode Available	0
A Communication and Computation Efficient Fully First-order Method for Decentralized Bilevel Optimization	Oct 18, 2024	Bilevel OptimizationFederated Learning	—Unverified	0
FedMSE: Federated learning for IoT network intrusion detection	Oct 18, 2024	Federated LearningIntrusion Detection	CodeCode Available	1
Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information	Oct 17, 2024	Conformal PredictionFederated Learning	—Unverified	0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning	Oct 17, 2024	Ensemble LearningFederated Learning	—Unverified	0
Investigating Effective Speaker Property Privacy Protection in Federated Learning for Speech Emotion Recognition	Oct 17, 2024	Emotion RecognitionFederated Learning	—Unverified	0
On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts	Oct 17, 2024	Depression DetectionFederated Learning	—Unverified	0
Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approach	Oct 17, 2024	Earth ObservationFederated Learning	—Unverified	0
Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity	Oct 17, 2024	Federated Learning	—Unverified	0
DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank Adaptation	Oct 16, 2024	Federated LearningPrivacy Preserving	CodeCode Available	0
FedCAP: Robust Federated Learning via Customized Aggregation and Personalization	Oct 16, 2024	Anomaly DetectionFederated Learning	CodeCode Available	0
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models	Oct 16, 2024	DecoderFederated Learning	—Unverified	0
FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning	Oct 16, 2024	Federated LearningTransfer Learning	—Unverified	0
TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logic	Oct 16, 2024	Decision MakingFederated Learning	—Unverified	0
Disentangling data distribution for Federated Learning	Oct 16, 2024	Federated Learning	—Unverified	0
Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks	Oct 16, 2024	Federated Learning	—Unverified	0
Age-of-Gradient Updates for Federated Learning over Random Access Channels	Oct 15, 2024	Federated Learning	—Unverified	0
Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study	Oct 15, 2024	Anomaly DetectionFederated Learning	—Unverified	0
Why Go Full? Elevating Federated Learning Through Partial Network Updates	Oct 15, 2024	Federated Learning	CodeCode Available	1
Data Quality Control in Federated Instruction-tuning of Large Language Models	Oct 15, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Backdoor Attack on Vertical Federated Graph Neural Network Learning	Oct 15, 2024	Backdoor AttackFederated Learning	—Unverified	0
FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning	Oct 15, 2024	DiversityDomain Generalization	CodeCode Available	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