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 5076–5100 of 6771 papers

Title	Date	Tasks	Status
Markov Chain Mirror Descent On Data Federation	Sep 26, 2023	Federated LearningStochastic Optimization	—Unverified
MarS-FL: Enabling Competitors to Collaborate in Federated Learning	Oct 26, 2021	Decision MakingFederated Learning	—Unverified
Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization	Nov 20, 2022	Android Malware DetectionFederated Learning	—Unverified
Massive MIMO for Serving Federated Learning and Non-Federated Learning Users	May 17, 2022	Federated Learning	—Unverified
MAS: Towards Resource-Efficient Federated Multiple-Task Learning	Jul 21, 2023	AllFederated Learning	—Unverified
Matching Pursuit Based Scheduling for Over-the-Air Federated Learning	Jun 14, 2022	Federated LearningScheduling	—Unverified
OPA: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning	Oct 29, 2024	Federated LearningPrivacy Preserving	—Unverified
Maverick-Aware Shapley Valuation for Client Selection in Federated Learning	May 21, 2024	Federated Learning	—Unverified
Maximizing Model Generalization for Machine Condition Monitoring with Self-Supervised Learning and Federated Learning	Apr 27, 2023	Domain AdaptationFederated Learning	—Unverified
Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning	Jan 14, 2025	Bayesian OptimisationFederated Learning	—Unverified
Maze Discovery using Multiple Robots via Federated Learning	Jun 25, 2024	ClassificationFederated Learning	—Unverified
MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing	Feb 7, 2020	Deep LearningFederated Learning	—Unverified
Measure Contribution of Participants in Federated Learning	Sep 17, 2019	BIG-bench Machine LearningFeature Importance	—Unverified
Measuring Heterogeneity in Machine Learning with Distributed Energy Distance	Jan 27, 2025	Federated Learning	—Unverified
Measuring Participant Contributions in Decentralized Federated Learning	May 29, 2025	Federated Learning	—Unverified
Mechanisms that Incentivize Data Sharing in Federated Learning	Jul 10, 2022	Federated Learning	—Unverified
MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentation	Mar 26, 2025	Federated LearningImage Segmentation	—Unverified
Membership Inference Attacks on Deep Regression Models for Neuroimaging	May 6, 2021	Deep LearningFederated Learning	—Unverified
Memory Backdoor Attacks on Neural Networks	Nov 21, 2024	Backdoor AttackFederated Learning	—Unverified
MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation	Nov 22, 2023	Edge-computingFederated Learning	—Unverified
Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios	Mar 14, 2024	Federated LearningPrivacy Preserving	—Unverified
Meta Federated Learning	Feb 10, 2021	Federated LearningPrivacy Preserving	—Unverified
Meta-Federated Learning: A Novel Approach for Real-Time Traffic Flow Management	Jan 28, 2025	Federated LearningManagement	—Unverified
Meta Federated Reinforcement Learning for Distributed Resource Allocation	Jul 6, 2023	Federated LearningMeta-Learning	—Unverified
Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning	Jun 23, 2024	DiversityFederated 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