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 5576–5600 of 6771 papers

Title	Date	Tasks	Status
A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity	Jun 11, 2025	Federated Learning	—Unverified
B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction	Oct 23, 2023	Federated LearningPrediction	—Unverified
Information-Geometric Barycenters for Bayesian Federated Learning	Dec 16, 2024	Bayesian InferenceDistributed Optimization	—Unverified
Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis	Oct 19, 2022	Backdoor AttackData Augmentation	—Unverified
Backdoor Attack on Vertical Federated Graph Neural Network Learning	Oct 15, 2024	Backdoor AttackFederated Learning	—Unverified
Backdoor attacks and defenses in feature-partitioned collaborative learning	Jul 7, 2020	Federated Learning	—Unverified
Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions	Mar 3, 2023	Backdoor AttackFederated Learning	—Unverified
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling	Apr 29, 2022	Federated LearningModel Poisoning	—Unverified
Backdoor Attacks in Peer-to-Peer Federated Learning	Jan 23, 2023	Backdoor AttackFederated Learning	—Unverified
Backdoor Attacks on Federated Meta-Learning	Jun 12, 2020	Federated LearningMeta-Learning	—Unverified
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection	Feb 21, 2022	backdoor defenseFederated Learning	—Unverified
Backdoor Federated Learning by Poisoning Backdoor-Critical Layers	Aug 8, 2023	Backdoor AttackFederated Learning	—Unverified
BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks	Nov 1, 2024	FairnessFederated Learning	—Unverified
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning	Jan 22, 2025	Federated LearningPersonalized Federated Learning	—Unverified
BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning	Jun 18, 2024	Data PoisoningFederated Learning	—Unverified
BadSFL: Backdoor Attack against Scaffold Federated Learning	Nov 25, 2024	Backdoor AttackFederated Learning	—Unverified
BadVFL: Backdoor Attacks in Vertical Federated Learning	Apr 18, 2023	Backdoor AttackFederated Learning	—Unverified
BaFFLe: Backdoor detection via Feedback-based Federated Learning	Nov 4, 2020	Federated LearningModel Poisoning	—Unverified
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS	Jan 1, 2021	Federated Learningimage-classification	—Unverified
Balanced Multi-modal Federated Learning via Cross-Modal Infiltration	Dec 31, 2023	Distributed ComputingFederated Learning	—Unverified
Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning	Jul 17, 2022	Federated LearningRobust Design	—Unverified
Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures	Jun 29, 2023	Federated LearningTransfer Learning	—Unverified
Balancing Client Participation in Federated Learning Using AoI	May 8, 2025	Federated LearningScheduling	—Unverified
Balancing Energy Efficiency and Distributional Robustness in Over-the-Air Federated Learning	Dec 22, 2023	Federated Learning	—Unverified
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation	May 9, 2023	Data PoisoningFederated 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