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 526–550 of 6771 papers

Title	Date	Tasks	Status	Hype	Score
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service	Aug 17, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1	5
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning	Jul 9, 2020	FairnessFederated 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
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings	Dec 26, 2021	AttributeEmotion Recognition	CodeCode Available	1	5
Federated Adaptation for Foundation Model-based Recommendations	May 8, 2024	Federated Learningmodel	CodeCode Available	1	5
Federated Class-Incremental Learning	Mar 22, 2022	class-incremental learningClass Incremental 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
Applied Federated Learning: Improving Google Keyboard Query Suggestions	Dec 7, 2018	BIG-bench Machine LearningFederated Learning	CodeCode Available	1	5
Federated Class-Incremental Learning with New-Class Augmented Self-Distillation	Jan 1, 2024	class-incremental learningClass Incremental Learning	CodeCode Available	1	5
FedAdapter: Efficient Federated Learning for Modern NLP	May 20, 2022	Federated Learning	CodeCode Available	1	5
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning	Sep 15, 2023	Data AugmentationDisentanglement	CodeCode Available	1	5
Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification	Jun 3, 2022	Deep LearningFederated Learning	CodeCode Available	1	5
DENSE: Data-Free One-Shot Federated Learning	Dec 23, 2021	Federated Learning	CodeCode Available	1	5
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Design	Jul 28, 2023	Experimental DesignFederated Learning	CodeCode Available	1	5
Backdoor Attacks on Federated Learning with Lottery Ticket Hypothesis	Sep 22, 2021	Backdoor AttackFederated Learning	CodeCode Available	1	5
A Dynamic Weighted Federated Learning for Android Malware Classification	Nov 23, 2022	ClassificationFederated Learning	CodeCode Available	1	5
Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better	Dec 18, 2021	Federated Learning	CodeCode Available	1	5
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning	Aug 21, 2023	Federated LearningKnowledge Distillation	CodeCode Available	1	5
ARFED: Attack-Resistant Federated averaging based on outlier elimination	Nov 8, 2021	Data PoisoningFederated 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
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent	Sep 11, 2020	Bayesian InferenceFederated Learning	CodeCode Available	1	5
Benchmarking Algorithms for Federated Domain Generalization	Jul 11, 2023	BenchmarkingDiversity	CodeCode Available	1	5
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection	Feb 7, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1	5
Benchmarking Differential Privacy and Federated Learning for BERT Models	Jun 26, 2021	BenchmarkingFederated Learning	CodeCode Available	1	5
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction	Mar 22, 2022	Federated Learningimage-classification	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