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 301–325 of 6771 papers

Title	Date	Tasks	Status	Hype	Score
Dopamine: Differentially Private Federated Learning on Medical Data	Jan 27, 2021	Federated Learning	CodeCode Available	1	5
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting	Feb 23, 2024	Federated Learning	CodeCode Available	1	5
Backdoor Attacks on Federated Learning with Lottery Ticket Hypothesis	Sep 22, 2021	Backdoor AttackFederated Learning	CodeCode Available	1	5
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning	Feb 9, 2024	AllFederated Learning	CodeCode Available	1	5
FedAdapter: Efficient Federated Learning for Modern NLP	May 20, 2022	Federated Learning	CodeCode Available	1	5
Evaluation Framework For Large-scale Federated Learning	Mar 3, 2020	Federated Learning	CodeCode Available	1	5
Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning	Apr 27, 2020	BIG-bench Machine LearningFederated Learning	CodeCode Available	1	5
Exploiting Label Skews in Federated Learning with Model Concatenation	Dec 11, 2023	Federated Learningimage-classification	CodeCode Available	1	5
Exploiting Shared Representations for Personalized Federated Learning	Feb 14, 2021	Federated LearningMeta-Learning	CodeCode Available	1	5
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT	Feb 15, 2022	Deep Reinforcement LearningEdge-computing	CodeCode Available	1	5
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training	Jun 1, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	1	5
Distantly Supervised Relation Extraction in Federated Settings	Aug 12, 2020	DenoisingFederated Learning	CodeCode Available	1	5
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning	Aug 23, 2021	Federated LearningMisconceptions	CodeCode Available	1	5
ARFED: Attack-Resistant Federated averaging based on outlier elimination	Nov 8, 2021	Data PoisoningFederated Learning	CodeCode Available	1	5
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching	Mar 12, 2025	FairnessFederated Learning	CodeCode Available	1	5
Fast-Convergent Federated Learning via Cyclic Aggregation	Oct 29, 2022	Federated Learning	CodeCode Available	1	5
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning	Feb 6, 2022	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	1	5
Fast Federated Learning in the Presence of Arbitrary Device Unavailability	Jun 8, 2021	Federated Learning	CodeCode Available	1	5
Applied Federated Learning: Improving Google Keyboard Query Suggestions	Dec 7, 2018	BIG-bench Machine LearningFederated Learning	CodeCode Available	1	5
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models	Aug 26, 2020	DiversityFederated Learning	CodeCode Available	1	5
Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detection	Mar 18, 2025	Federated LearningGenerative Adversarial Network	CodeCode Available	1	5
A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning	Mar 18, 2021	Federated Learning	CodeCode Available	1	5
Bias Propagation in Federated Learning	Sep 5, 2023	FairnessFederated Learning	CodeCode Available	1	5
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise	Dec 20, 2023	Federated LearningImage Segmentation	CodeCode Available	1	5
A Survey for Federated Learning Evaluations: Goals and Measures	Aug 23, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1	5

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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