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 4976–5000 of 6771 papers

Title	Date	Tasks	Status
Learning from straggler clients in federated learning	Mar 14, 2024	Federated Learning	—Unverified
Learning Personalized Brain Functional Connectivity of MDD Patients from Multiple Sites via Federated Bayesian Networks	Jan 6, 2023	Federated LearningFunctional Connectivity	—Unverified
Learnings from Federated Learning in the Real world	Feb 8, 2022	Federated LearningNatural Language Understanding	—Unverified
Learning To Collaborate in Decentralized Learning of Personalized Models	Jan 1, 2022	Federated Learningimage-classification	—Unverified
Learning to Detect Malicious Clients for Robust Federated Learning	Feb 1, 2020	Federated Learningimage-classification	—Unverified
Learning to Generate Image Embeddings with User-level Differential Privacy	Nov 20, 2022	Federated Learningimage-classification	—Unverified
Learning Tokenization in Private Federated Learning with Sub-Word Model Sampling	Nov 16, 2021	Federated LearningLanguage Modeling	—Unverified
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images	Jun 30, 2022	Federated LearningObject Recognition	—Unverified
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels	Feb 5, 2021	Federated LearningGeneralization Bounds	—Unverified
Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data	Jul 7, 2020	Federated Learning	—Unverified
Learn More by Using Less: Distributed Learning with Energy-Constrained Devices	Dec 3, 2024	Federated LearningPrivacy Preserving	—Unverified
Learn to Forget: Machine Unlearning via Neuron Masking	Mar 24, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning	Jul 26, 2021	Federated Learning	—Unverified
LEGO: Language Model Building Blocks	Oct 23, 2024	Federated LearningLanguage Modeling	—Unverified
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning	May 3, 2023	feature selectionFederated Learning	—Unverified
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning	Apr 30, 2024	Backdoor AttackDataset Distillation	—Unverified
Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance	Jun 9, 2022	Federated LearningUser Simulation	—Unverified
Leveraging feature communication in federated learning for remote sensing image classification	Mar 20, 2024	ClassificationFederated Learning	—Unverified
Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks	Mar 5, 2024	Cloud ComputingDeep Reinforcement Learning	—Unverified
Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures	Mar 5, 2024	Federated LearningPrivacy Preserving	—Unverified
Leveraging Foundation Models for Efficient Federated Learning in Resource-restricted Edge Networks	Sep 14, 2024	Data-free Knowledge DistillationFederated Learning	—Unverified
Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality	Jun 16, 2024	Federated LearningImage-text Classification	—Unverified
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning	Nov 14, 2023	Federated Learning	—Unverified
Leveraging Function Space Aggregation for Federated Learning at Scale	Nov 17, 2023	Distributed OptimizationFederated Learning	—Unverified
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training	May 4, 2023	Federated Learning	—Unverified

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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