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 5701–5725 of 6771 papers

Title	Date	Tasks	Status
Adaptive Gradient Clipping for Robust Federated Learning	May 23, 2024	ARCFederated Learning	—Unverified
Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration	Oct 9, 2024	Federated Learning	—Unverified
Boosting with Multiple Sources	Dec 1, 2021	Federated Learning	—Unverified
BPFISH: Blockchain and Privacy-preserving FL Inspired Smart Healthcare	Jul 24, 2022	Federated LearningPrivacy Preserving	—Unverified
Brain MRI Screening Tool with Federated Learning	Nov 23, 2023	Federated Learning	—Unverified
Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification	Apr 24, 2024	Federated Learningimage-classification	—Unverified
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning	May 16, 2019	Brain SegmentationFederated Learning	—Unverified
Brain Tumor Detection in MRI Based on Federated Learning with YOLOv11	Mar 6, 2025	Federated Learning	—Unverified
Brave: Byzantine-Resilient and Privacy-Preserving Peer-to-Peer Federated Learning	Jan 10, 2024	Federated Learningimage-classification	—Unverified
Breaking Data Silos: Towards Open and Scalable Mobility Foundation Models via Generative Continual Learning	Jun 7, 2025	Continual LearningFederated Learning	—Unverified
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning	Jun 22, 2024	Federated LearningInference Attack	—Unverified
Breaking the centralized barrier for cross-device federated learning	Dec 1, 2021	Federated Learning	—Unverified
Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training	Apr 20, 2024	Federated Learning	—Unverified
Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting	Oct 12, 2024	Federated Learning	—Unverified
BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model	Oct 20, 2023	Federated Learning	—Unverified
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning	Apr 29, 2024	Bayesian OptimizationBinary Classification	—Unverified
Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation	Apr 14, 2024	Earth ObservationFederated Learning	—Unverified
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning	Sep 30, 2023	Federated LearningModel Compression	—Unverified
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering	Aug 22, 2022	ClusteringFederated Learning	—Unverified
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments	Oct 21, 2021	Federated LearningTransfer Learning	—Unverified
Distributed Non-Convex Optimization with One-Bit Compressors on Heterogeneous Data: Efficient and Resilient Algorithms	Oct 3, 2022	Federated LearningPrivacy Preserving	—Unverified
Budgeted Online Model Selection and Fine-Tuning via Federated Learning	Jan 19, 2024	Federated LearningModel Selection	—Unverified
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning	Nov 16, 2020	Federated Learning	—Unverified
Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning	Jun 5, 2024	Federated Learning	—Unverified
Buffer is All You Need: Defending Federated Learning against Backdoor Attacks under Non-iids via Buffering	Mar 30, 2025	AllContrastive 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