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 4001–4025 of 6771 papers

Title	Date	Tasks	Status	Hype
FedCC: Robust Federated Learning against Model Poisoning Attacks	Dec 5, 2022	Federated Learningmodel	—Unverified	0
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding	Dec 4, 2022	Federated Learningimage-classification	—Unverified	0
Security Analysis of SplitFed Learning	Dec 4, 2022	Federated LearningModel Poisoning	—Unverified	0
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning	Dec 3, 2022	Federated LearningModel Compression	—Unverified	0
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction	Dec 3, 2022	Federated Learningmodel	CodeCode Available	1
Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework	Dec 3, 2022	Federated Learningimage-classification	—Unverified	0
PGFed: Personalize Each Client's Global Objective for Federated Learning	Dec 2, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	0
Self-supervised On-device Federated Learning from Unlabeled Streams	Dec 2, 2022	Contrastive LearningFederated Learning	—Unverified	0
Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learning	Dec 2, 2022	Data AugmentationEdge-computing	—Unverified	0
Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network	Dec 2, 2022	Federated Learning	—Unverified	0
FedALA: Adaptive Local Aggregation for Personalized Federated Learning	Dec 2, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Faster Adaptive Federated Learning	Dec 2, 2022	Federated Learningimage-classification	—Unverified	0
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning	Dec 2, 2022	Federated LearningMeta-Learning	—Unverified	0
PiPar: Pipeline Parallelism for Collaborative Machine Learning	Dec 1, 2022	Federated LearningPrivacy Preserving	—Unverified	0
Towards Practical Few-shot Federated NLP	Dec 1, 2022	Data AugmentationFederated Learning	—Unverified	0
Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems	Dec 1, 2022	Federated LearningPrivacy Preserving	—Unverified	0
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning	Dec 1, 2022	Federated LearningReconstruction Attack	—Unverified	0
Hijack Vertical Federated Learning Models As One Party	Dec 1, 2022	Federated LearningVertical Federated Learning	—Unverified	0
Vertical Federated Learning: A Structured Literature Review	Dec 1, 2022	Federated LearningVertical Federated Learning	—Unverified	0
Early prediction of the risk of ICU mortality with Deep Federated Learning	Dec 1, 2022	Federated LearningICU Mortality	CodeCode Available	0
FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning	Nov 30, 2022	Federated Learning	—Unverified	0
On the Design of Communication-Efficient Federated Learning for Health Monitoring	Nov 30, 2022	Federated LearningTransfer Learning	—Unverified	0
Privacy-Preserving Federated Deep Clustering based on GAN	Nov 30, 2022	ClusteringDeep Clustering	—Unverified	0
Adap DP-FL: Differentially Private Federated Learning with Adaptive Noise	Nov 29, 2022	Federated Learning	—Unverified	0
Performance Evaluation, Optimization and Dynamic Decision in Blockchain Systems: A Recent Overview	Nov 29, 2022	Deep Reinforcement LearningFederated 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