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 2176–2200 of 6771 papers

Title	Date	Tasks	Status
Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration	Oct 9, 2024	Federated Learning	—Unverified
Dynamic Heterogeneous Federated Learning with Multi-Level Prototypes	Dec 15, 2023	Federated LearningPrivacy Preserving	—Unverified
Dynamic Gradient Aggregation for Federated Domain Adaptation	Jun 14, 2021	Domain AdaptationFederated Learning	—Unverified
Adaptive Gradient Clipping for Robust Federated Learning	May 23, 2024	ARCFederated Learning	—Unverified
Dynamic Fusion based Federated Learning for COVID-19 Detection	Sep 22, 2020	BIG-bench Machine LearningDecision Making	—Unverified
Boosting multi-demographic federated learning for chest x-ray analysis using general-purpose self-supervised representations	Apr 11, 2025	Federated LearningMedical Image Analysis	—Unverified
DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learning	Jul 16, 2023	Federated LearningScheduling	—Unverified
Boosting Federated Learning Convergence with Prototype Regularization	Jul 20, 2023	Federated Learning	—Unverified
An Efficient Virtual Data Generation Method for Reducing Communication in Federated Learning	Jun 21, 2023	Federated Learning	—Unverified
Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers	Apr 1, 2024	Federated LearningLoad Forecasting	—Unverified
Achieving Personalized Federated Learning with Sparse Local Models	Jan 27, 2022	Federated LearningPersonalized Federated Learning	—Unverified
Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles	Jan 1, 2021	Federated Learning	—Unverified
Dynamic Federated Learning	Feb 20, 2020	Federated Learning	—Unverified
Boosting Federated Domain Generalization: Understanding the Role of Advanced Pre-Trained Architectures	Sep 20, 2024	Domain GeneralizationFederated Learning	—Unverified
Dynamic Fair Federated Learning Based on Reinforcement Learning	Nov 2, 2023	FairnessFederated Learning	—Unverified
Dynamic Differential-Privacy Preserving SGD	Oct 30, 2021	Federated Learningimage-classification	—Unverified
Boosting Fairness and Robustness in Over-the-Air Federated Learning	Mar 7, 2024	FairnessFederated Learning	—Unverified
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application	Jun 21, 2022	Face RecognitionFederated Learning	—Unverified
Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection	Apr 9, 2024	Federated Learning	—Unverified
Dynamic Clustering in Federated Learning	Dec 7, 2020	ClusteringFederated Learning	—Unverified
Dynamic backdoor attacks against federated learning	Nov 15, 2020	Adversarial AttackBIG-bench Machine Learning	—Unverified
Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources	Sep 5, 2022	Federated Learning	—Unverified
An Efficient Imbalance-Aware Federated Learning Approach for Wearable Healthcare with Autoregressive Ratio Observation	Oct 23, 2023	Edge-computingFederated Learning	—Unverified
Dynamic Attention-based Communication-Efficient Federated Learning	Aug 12, 2021	Federated Learning	—Unverified
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning	Jul 12, 2024	Anomaly DetectionBackdoor Attack	—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