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 2576–2600 of 6771 papers

Title	Date	Tasks	Status
Balancing Client Participation in Federated Learning Using AoI	May 8, 2025	Federated LearningScheduling	—Unverified
A Knowledge Distillation-Based Backdoor Attack in Federated Learning	Aug 12, 2022	Backdoor AttackFederated Learning	—Unverified
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning	Dec 11, 2020	Federated Learning	—Unverified
Delayed Random Partial Gradient Averaging for Federated Learning	Dec 28, 2024	Federated Learning	—Unverified
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning	Dec 1, 2021	Federated Learning	—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
Delay-Aware Hierarchical Federated Learning	Mar 22, 2023	Federated Learning	—Unverified
Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theory	Mar 31, 2021	Federated Learning	—Unverified
Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning	Jul 17, 2022	Federated LearningRobust Design	—Unverified
DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg	Apr 6, 2022	Federated Learning	—Unverified
DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning	Aug 1, 2022	Federated LearningPrivacy Preserving	—Unverified
Balanced Multi-modal Federated Learning via Cross-Modal Infiltration	Dec 31, 2023	Distributed ComputingFederated Learning	—Unverified
Defense via Behavior Attestation against Attacks in Connected and Automated Vehicles based Federated Learning Systems	Mar 14, 2024	Federated Learning	—Unverified
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS	Jan 1, 2021	Federated Learningimage-classification	—Unverified
A Joint Gradient and Loss Based Clustered Federated Learning Design	Nov 22, 2023	ClusteringFederated Learning	—Unverified
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits	Dec 6, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Defense Against Gradient Leakage Attacks via Learning to Obscure Data	Jun 1, 2022	Federated LearningPrivacy Preserving	—Unverified
Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning	May 15, 2025	Backdoor AttackFederated Learning	—Unverified
Defending Label Inference Attacks in Split Learning under Regression Setting	Aug 18, 2023	Federated LearningPrivacy Preserving	—Unverified
Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning	Dec 10, 2021	Federated LearningInference Attack	—Unverified
BaFFLe: Backdoor detection via Feedback-based Federated Learning	Nov 4, 2020	Federated LearningModel Poisoning	—Unverified
Defending against the Label-flipping Attack in Federated Learning	Jul 5, 2022	Federated Learning	—Unverified
BadVFL: Backdoor Attacks in Vertical Federated Learning	Apr 18, 2023	Backdoor AttackFederated Learning	—Unverified
Defending against Reconstruction Attack in Vertical Federated Learning	Jul 21, 2021	Federated LearningPrivacy Preserving	—Unverified
BadSFL: Backdoor Attack against Scaffold Federated Learning	Nov 25, 2024	Backdoor AttackFederated 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