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 2051–2075 of 6771 papers

Title	Date	Tasks	Status	Hype
FLARE: A New Federated Learning Framework with Adjustable Learning Rates over Resource-Constrained Wireless Networks	Apr 23, 2024	Federated LearningScheduling	—Unverified	0
Leverage Variational Graph Representation For Model Poisoning on Federated Learning	Apr 23, 2024	Federated LearningModel Poisoning	CodeCode Available	0
Distributed Learning for Wi-Fi AP Load Prediction	Apr 22, 2024	Federated LearningKnowledge Distillation	—Unverified	0
Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning	Apr 22, 2024	Backdoor AttackData Poisoning	—Unverified	0
Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction	Apr 22, 2024	Autonomous VehiclesFederated Learning	—Unverified	0
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks	Apr 22, 2024	Federated Learning	CodeCode Available	0
Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments	Apr 22, 2024	CPUFederated Learning	—Unverified	0
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning	Apr 22, 2024	Data-free Knowledge DistillationFederated Learning	—Unverified	0
Machine Learning Techniques for MRI Data Processing at Expanding Scale	Apr 22, 2024	Federated LearningRepresentation Learning	—Unverified	0
Fair Concurrent Training of Multiple Models in Federated Learning	Apr 22, 2024	FairnessFederated Learning	—Unverified	0
FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization	Apr 21, 2024	Federated LearningQuantization	—Unverified	0
FedTrans: Efficient Federated Learning via Multi-Model Transformation	Apr 21, 2024	Federated Learning	—Unverified	0
A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning	Apr 20, 2024	Federated LearningQuantization	—Unverified	0
Intelligent Agents for Auction-based Federated Learning: A Survey	Apr 20, 2024	Federated LearningSurvey	—Unverified	0
EHRFL: Federated Learning Framework for Institution-Specific Model Construction using Electronic Health Records	Apr 20, 2024	Federated Learning	CodeCode Available	1
Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training	Apr 20, 2024	Federated Learning	—Unverified	0
Collaborative Visual Place Recognition through Federated Learning	Apr 20, 2024	Contrastive LearningFederated Learning	—Unverified	0
PAFedFV: Personalized and Asynchronous Federated Learning for Finger Vein Recognition	Apr 20, 2024	Federated LearningFinger Vein Recognition	—Unverified	0
MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning	Apr 20, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Personalized Wireless Federated Learning for Large Language Models	Apr 20, 2024	Federated Learning	—Unverified	0
End-to-End Verifiable Decentralized Federated Learning	Apr 19, 2024	Federated Learning	CodeCode Available	0
CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance	Apr 19, 2024	Federated Learning	—Unverified	0
KoReA-SFL: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgetting	Apr 19, 2024	Federated Learning	—Unverified	0
Towards Multi-modal Transformers in Federated Learning	Apr 18, 2024	Federated LearningPrivacy Preserving	CodeCode Available	1
FedEval-LLM: Federated Evaluation of Large Language Models on Downstream Tasks with Collective Wisdom	Apr 18, 2024	Federated LearningPrivacy Preserving	—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