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 676–700 of 6771 papers

Title	Date	Tasks	Status	Hype	Score
Efficient On-device Training via Gradient Filtering	Jan 1, 2023	Federated Learningimage-classification	CodeCode Available	1	5
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation	Mar 17, 2022	Entity EmbeddingsFederated Learning	CodeCode Available	1	5
Differentially Private Federated Learning: A Client Level Perspective	Dec 20, 2017	Federated LearningPrivacy Preserving	CodeCode Available	1	5
FedAdapter: Efficient Federated Learning for Modern NLP	May 20, 2022	Federated Learning	CodeCode Available	1	5
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources	Mar 10, 2021	Federated Learning	CodeCode Available	1	5
Efficient Personalized Federated Learning via Sparse Model-Adaptation	May 4, 2023	Federated Learningmodel	CodeCode Available	1	5
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization	Mar 18, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	1	5
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning	Jul 9, 2020	FairnessFederated Learning	CodeCode Available	1	5
EHRFL: Federated Learning Framework for Institution-Specific Model Construction using Electronic Health Records	Apr 20, 2024	Federated Learning	CodeCode Available	1	5
Decentralized Federated Learning: A Segmented Gossip Approach	Aug 21, 2019	Federated Learning	CodeCode Available	1	5
Improving Transferability of Network Intrusion Detection in a Federated Learning Setup	Jan 7, 2024	Federated LearningIntrusion Detection	CodeCode Available	1	5
Benchmarking Algorithms for Federated Domain Generalization	Jul 11, 2023	BenchmarkingDiversity	CodeCode Available	1	5
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings	Dec 26, 2021	AttributeEmotion Recognition	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
Inverting Gradients - How easy is it to break privacy in federated learning?	Dec 1, 2020	Federated Learning	CodeCode Available	1	5
End-to-End Speech Recognition from Federated Acoustic Models	Apr 29, 2021	2k4k	CodeCode Available	1	5
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting	Feb 23, 2024	Federated Learning	CodeCode Available	1	5
Energy-Latency Attacks via Sponge Poisoning	Mar 14, 2022	ClassificationFederated Learning	CodeCode Available	1	5
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging	May 17, 2022	Federated LearningMedical Image Analysis	CodeCode Available	1	5
Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach	Feb 24, 2021	Federated LearningGenerative Adversarial Network	CodeCode Available	1	5
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning	Nov 30, 2021	Federated Learning	CodeCode Available	1	5
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage	Mar 29, 2022	Bayesian OptimizationFederated Learning	CodeCode Available	1	5
Proportional Fairness in Federated Learning	Feb 3, 2022	FairnessFederated Learning	CodeCode Available	1	5
Learn distributed GAN with Temporary Discriminators	Jul 17, 2020	Federated 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

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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