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 226–250 of 6771 papers

Title	Date	Tasks	Status	Hype
Non-IID Quantum Federated Learning with One-shot Communication Complexity	Sep 2, 2022	Federated Learning	CodeCode Available	1
A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee	Aug 1, 2021	Federated Learning	CodeCode Available	1
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning	Jun 11, 2021	Federated LearningVertical Federated Learning	CodeCode Available	1
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT	Feb 15, 2022	Deep Reinforcement LearningEdge-computing	CodeCode Available	1
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation	Apr 14, 2022	Federated LearningPrivacy Preserving	CodeCode Available	1
Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity	May 26, 2024	DiversityFairness	CodeCode Available	1
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching	Mar 12, 2025	FairnessFederated Learning	CodeCode Available	1
Fast Federated Learning by Balancing Communication Trade-Offs	May 23, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
Fast Federated Learning in the Presence of Arbitrary Device Unavailability	Jun 8, 2021	Federated Learning	CodeCode Available	1
ARFED: Attack-Resistant Federated averaging based on outlier elimination	Nov 8, 2021	Data PoisoningFederated Learning	CodeCode Available	1
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee	Oct 26, 2021	Decision MakingFederated Learning	CodeCode Available	1
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources	Mar 10, 2021	Federated Learning	CodeCode Available	1
Feature Inference Attack on Model Predictions in Vertical Federated Learning	Oct 20, 2020	Federated LearningInference Attack	CodeCode Available	1
A Distributed Trust Framework for Privacy-Preserving Machine Learning	Jun 3, 2020	BIG-bench Machine LearningData Poisoning	CodeCode Available	1
FedALA: Adaptive Local Aggregation for Personalized Federated Learning	Dec 2, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	1
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction	May 26, 2022	Domain GeneralizationFederated Learning	CodeCode Available	1
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning	Feb 4, 2021	Federated LearningUnsupervised Pre-training	CodeCode Available	1
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing	Jul 20, 2023	Federated LearningOne-Shot Learning	CodeCode Available	1
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications	Apr 24, 2023	Federated Learning	CodeCode Available	1
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization	Feb 15, 2021	Autonomous DrivingFederated Learning	CodeCode Available	1
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning	Jul 9, 2020	FairnessFederated Learning	CodeCode Available	1
Asynchronous Federated Continual Learning	Apr 7, 2023	Continual LearningFederated Learning	CodeCode Available	1
FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity	Nov 14, 2022	Federated LearningKnowledge Distillation	CodeCode Available	1
FedCM: Federated Learning with Client-level Momentum	Jun 21, 2021	Federated Learning	CodeCode Available	1
ACTION: Augmentation and Computation Toolbox for Brain Network Analysis with Functional MRI	May 10, 2024	Data AugmentationFederated Learning	CodeCode Available	1

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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