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 6626–6650 of 6771 papers

Title	Date	Tasks	Status
Improving Communication Efficiency of Federated Distillation via Accumulating Local Updates	Dec 7, 2023	Federated Learning	CodeCode Available
RFID based Health Adherence Medicine Case Using Fair Federated Learning	Aug 21, 2024	Federated Learning	CodeCode Available
Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods	May 19, 2023	FairnessFederated Learning	CodeCode Available
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels	Dec 19, 2023	Federated LearningLearning with noisy labels	CodeCode Available
Improving Federated Aggregation with Deep Unfolding Networks	Jun 30, 2023	Federated Learning	CodeCode Available
One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity	Apr 18, 2024	DiversityFederated Learning	CodeCode Available
RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation	May 24, 2024	Federated Learning	CodeCode Available
Improving Federated Learning Personalization via Model Agnostic Meta Learning	Sep 27, 2019	Federated LearningMeta-Learning	CodeCode Available
Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space	Dec 15, 2023	Bayesian InferenceFederated Learning	CodeCode Available
FedDebug: Systematic Debugging for Federated Learning Applications	Jan 9, 2023	Fault localizationFederated Learning	CodeCode Available
Venomancer: Towards Imperceptible and Target-on-Demand Backdoor Attacks in Federated Learning	Jul 3, 2024	Backdoor AttackFederated Learning	CodeCode Available
Privacy-Preserving Classification with Secret Vector Machines	Jul 8, 2019	ClassificationFederated Learning	CodeCode Available
Analysis of Privacy Leakage in Federated Large Language Models	Mar 2, 2024	Federated Learning	CodeCode Available
Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers	Nov 3, 2022	Federated Learning	CodeCode Available
Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory	Sep 7, 2023	ClusteringContinual Learning	CodeCode Available
Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness	Sep 24, 2023	Computational EfficiencyFederated Learning	CodeCode Available
Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image Augmentation	Dec 12, 2021	Federated LearningImage Augmentation	CodeCode Available
Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy	Dec 8, 2022	Federated LearningMath	CodeCode Available
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM	Jul 20, 2022	DenoisingFederated Learning	CodeCode Available
Improving Response Time of Home IoT Services in Federated Learning	Feb 28, 2022	Distributed ComputingFederated Learning	CodeCode Available
Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models (Extended Version)	Jul 11, 2024	Federated LearningPrivacy Preserving	CodeCode Available
RiM: Record, Improve and Maintain Physical Well-being using Federated Learning	May 9, 2025	Federated LearningPrivacy Preserving	CodeCode Available
When Do Curricula Work in Federated Learning?	Dec 24, 2022	Federated Learning	CodeCode Available
FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification	Feb 24, 2023	Federated Learning	CodeCode Available
UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data	Jun 9, 2025	Federated Learning	CodeCode Available

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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