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 6151–6175 of 6771 papers

Title	Date	Tasks	Status
Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels	Aug 19, 2022	Federated LearningLearning with noisy labels	CodeCode Available
Fidel: Reconstructing Private Training Samples from Weight Updates in Federated Learning	Jan 1, 2021	Federated Learning	CodeCode Available
FilFL: Client Filtering for Optimized Client Participation in Federated Learning	Feb 13, 2023	Federated Learning	CodeCode Available
Accurate Forgetting for Heterogeneous Federated Continual Learning	Feb 20, 2025	Continual LearningFederated Learning	CodeCode Available
Financial Data Analysis with Robust Federated Logistic Regression	Apr 28, 2025	Federated LearningMulti-class Classification	CodeCode Available
One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them	May 12, 2019	Federated Learning	CodeCode Available
Federated Learning: Challenges, Methods, and Future Directions	Aug 21, 2019	BIG-bench Machine LearningDistributed Optimization	CodeCode Available
Robust Smart Home Face Recognition under Starving Federated Data	Nov 10, 2022	Adversarial AttackFace Recognition	CodeCode Available
Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) Framework	Apr 26, 2023	Federated Learning	CodeCode Available
Federated Learning with Intermediate Representation Regularization	Oct 28, 2022	Federated Learning	CodeCode Available
STDLens: Model Hijacking-Resilient Federated Learning for Object Detection	Mar 21, 2023	Federated Learningobject-detection	CodeCode Available
Maximum Knowledge Orthogonality Reconstruction with Gradients in Federated Learning	Oct 30, 2023	Federated Learning	CodeCode Available
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentation	Mar 27, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available
MDA: Availability-Aware Federated Learning Client Selection	Nov 25, 2022	Federated Learning	CodeCode Available
Fingerprint Attack: Client De-Anonymization in Federated Learning	Sep 12, 2023	ClusteringFederated Learning	CodeCode Available
MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets	Nov 9, 2018	Federated Learning	CodeCode Available
Robustness analytics to data heterogeneity in edge computing	Feb 12, 2020	Edge-computingFederated Learning	CodeCode Available
CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection	Apr 2, 2025	Federated LearningIntrusion Detection	CodeCode Available
PARDON: Privacy-Aware and Robust Federated Domain Generalization	Oct 30, 2024	Contrastive LearningDomain Generalization	CodeCode Available
DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank Adaptation	Oct 16, 2024	Federated LearningPrivacy Preserving	CodeCode Available
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning	Feb 24, 2017	Deep LearningFederated Learning	CodeCode Available
FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder Decomposition	Aug 30, 2024	DecoderFederated Learning	CodeCode Available
Federated Learning With Individualized Privacy Through Client Sampling	Jan 29, 2025	Federated Learning	CodeCode Available
Exploring Data Redundancy in Real-world Image Classification through Data Selection	Jun 25, 2023	Active LearningContinual Learning	CodeCode Available
F-KANs: Federated Kolmogorov-Arnold Networks	Jul 29, 2024	ClassificationFederated Learning	CodeCode Available

Show:10 25 50

← PrevPage 247 of 271Next →

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