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 1501–1525 of 6771 papers

Title	Date	Tasks	Status	Score
FedCert: Federated Accuracy Certification	Oct 4, 2024	Federated Learning	CodeCode Available	5
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning	Nov 16, 2021	Federated LearningPrivacy Preserving	CodeCode Available	5
FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learning	Dec 16, 2024	Federated LearningImage Generation	CodeCode Available	5
FedCAP: Robust Federated Learning via Customized Aggregation and Personalization	Oct 16, 2024	Anomaly DetectionFederated Learning	CodeCode Available	5
A Federated Approach to Predicting Emojis in Hindi Tweets	Nov 11, 2022	Federated Learning	CodeCode Available	5
FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning	Oct 15, 2024	DiversityDomain Generalization	CodeCode Available	5
Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning	Jan 21, 2025	Federated Learning	CodeCode Available	5
Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution Strategies	Nov 5, 2023	Federated LearningPrivacy Preserving	CodeCode Available	5
FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learning	Feb 24, 2025	Federated Learning	CodeCode Available	5
Improving Response Time of Home IoT Services in Federated Learning	Feb 28, 2022	Distributed ComputingFederated Learning	CodeCode Available	5
FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Data	Jun 25, 2025	Federated LearningKnowledge Distillation	CodeCode Available	5
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data	May 6, 2022	Binary ClassificationFederated Learning	CodeCode Available	5
Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning	Jun 20, 2024	Federated LearningModel Poisoning	CodeCode Available	5
InFL-UX: A Toolkit for Web-Based Interactive Federated Learning	Mar 6, 2025	Federated Learning	CodeCode Available	5
Communication-Efficient ADMM-based Federated Learning	Oct 28, 2021	Federated Learning	CodeCode Available	5
Intelligent Client Selection for Federated Learning using Cellular Automata	Oct 1, 2023	Federated Learning	CodeCode Available	5
FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizer	May 2, 2023	Federated Learning	CodeCode Available	5
Interpretability of Fine-grained Classification of Sadness and Depression	Mar 20, 2022	ClassificationFederated Learning	CodeCode Available	5
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID Data	Sep 9, 2024	Brain Tumor ClassificationFederated Learning	CodeCode Available	5
Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks	Apr 22, 2024	Federated Learning	CodeCode Available	5
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal Transport	Nov 15, 2024	Activity RecognitionFederated Learning	CodeCode Available	5
IODeep: an IOD for the introduction of deep learning in the DICOM standard	Nov 10, 2023	DiagnosticFederated Learning	CodeCode Available	5
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks	Sep 17, 2019	Federated Learning	CodeCode Available	5
IronForge: An Open, Secure, Fair, Decentralized Federated Learning	Jan 7, 2023	FairnessFederated Learning	CodeCode Available	5
FedAPM: Federated Learning via ADMM with Partial Model Personalization	Jun 5, 2025	Federated Learning	CodeCode Available	5

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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