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 5301–5325 of 6771 papers

Title	Date	Tasks	Status
A-LAQ: Adaptive Lazily Aggregated Quantized Gradient	Oct 31, 2022	Federated Learning	—Unverified
Declarative Privacy-Preserving Inference Queries	Jan 22, 2024	Federated LearningManagement	—Unverified
Algorithm Fairness in AI for Medicine and Healthcare	Oct 1, 2021	DisentanglementFairness	—Unverified
A Life-long Learning Intrusion Detection System for 6G-Enabled IoV	Jul 22, 2024	class-incremental learningClass Incremental Learning	—Unverified
A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging	Jun 6, 2023	Federated Learning	—Unverified
Aligning Beam with Imbalanced Multi-modality: A Generative Federated Learning Approach	Apr 21, 2025	Federated Learning	—Unverified
A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems	Mar 17, 2025	Computational EfficiencyFederated Learning	—Unverified
HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning	Dec 1, 2022	Federated LearningReconstruction Attack	—Unverified
Almost Cost-Free Communication in Federated Best Arm Identification	Aug 19, 2022	Federated Learning	—Unverified
Almost Tight Error Bounds on Differentially Private Continual Counting	Nov 9, 2022	Federated Learning	—Unverified
-Weighted Federated Adversarial Training	Sep 29, 2021	Adversarial AttackFederated Learning	—Unverified
A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systems	Dec 6, 2023	Dimensionality ReductionFederated Learning	—Unverified
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning	May 28, 2023	Federated LearningMeta-Learning	—Unverified
A Metamodel and Framework for AGI	Aug 28, 2020	Federated Learningobject-detection	—Unverified
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice	Feb 11, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
AMI-FML: A Privacy-Preserving Federated Machine Learning Framework for AMI	Sep 13, 2021	BIG-bench Machine Learningenergy management	—Unverified
A Modified UDP for Federated Learning Packet Transmissions	Aug 11, 2022	Federated Learning	—Unverified
(Amplified) Banded Matrix Factorization: A unified approach to private training	Jun 13, 2023	Federated Learning	—Unverified
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning	Mar 7, 2023	Federated LearningPrivacy Preserving	—Unverified
AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling	May 27, 2025	Federated Learning	—Unverified
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning	Jan 9, 2022	Federated LearningMulti-agent Reinforcement Learning	—Unverified
A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications	Sep 24, 2024	Anomaly DetectionFederated Learning	—Unverified
A Multi-Modal Federated Learning Framework for Remote Sensing Image Classification	Mar 13, 2025	Federated Learningimage-classification	—Unverified
A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning	Sep 18, 2023	Federated LearningVertical Federated Learning	—Unverified
A Multivocal Literature Review on Privacy and Fairness in Federated Learning	Aug 16, 2024	FairnessFederated 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