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 3101–3125 of 6771 papers

Title	Date	Tasks	Status
TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients	Jan 22, 2024	Federated Learning	CodeCode Available
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning	Jan 22, 2024	Computational EfficiencyFederated Learning	—Unverified
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems	Jan 22, 2024	Federated Learning	—Unverified
LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning	Jan 22, 2024	Federated LearningSelf-Supervised Learning	—Unverified
Declarative Privacy-Preserving Inference Queries	Jan 22, 2024	Federated LearningManagement	—Unverified
Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting	Jan 22, 2024	Decision MakingFederated Learning	—Unverified
Attention on Personalized Clinical Decision Support System: Federated Learning Approach	Jan 22, 2024	Disease PredictionFederated Learning	CodeCode Available
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity	Jan 22, 2024	Federated LearningGraph Learning	—Unverified
Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces	Jan 22, 2024	Federated LearningMulti-Task Learning	—Unverified
Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach	Jan 22, 2024	Federated LearningPrivacy Preserving	—Unverified
Differentially-Private Multi-Tier Federated Learning	Jan 21, 2024	Federated Learning	—Unverified
FedRKG: A Privacy-preserving Federated Recommendation Framework via Knowledge Graph Enhancement	Jan 20, 2024	Federated LearningPrivacy Preserving	CodeCode Available
Budgeted Online Model Selection and Fine-Tuning via Federated Learning	Jan 19, 2024	Federated LearningModel Selection	—Unverified
Federated Learning with Dual Attention for Robust Modulation Classification under Attacks	Jan 19, 2024	Data PoisoningFederated Learning	—Unverified
Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection	Jan 19, 2024	Federated LearningFraud Detection	—Unverified
Improving Local Training in Federated Learning via Temperature Scaling	Jan 18, 2024	Federated Learning	—Unverified
Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs	Jan 18, 2024	ClusteringDiversity	CodeCode Available
Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks	Jan 18, 2024	Federated LearningPrivacy Preserving	—Unverified
Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilities	Jan 18, 2024	Federated LearningPrivacy Preserving	—Unverified
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks	Jan 18, 2024	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients	Jan 17, 2024	Federated Learning	—Unverified
Federated Unlearning for Human Activity Recognition	Jan 17, 2024	Activity RecognitionFederated Learning	—Unverified
Technical Report: On the Convergence of Gossip Learning in the Presence of Node Inaccessibility	Jan 17, 2024	Federated Learning	—Unverified
A GAN-based data poisoning framework against anomaly detection in vertical federated learning	Jan 17, 2024	Anomaly DetectionData Poisoning	—Unverified
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data	Jan 17, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available

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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