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 151–175 of 6771 papers

Title	Date	Tasks	Status	Hype
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning	Feb 6, 2022	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	1
Backdoor Attacks on Federated Learning with Lottery Ticket Hypothesis	Sep 22, 2021	Backdoor AttackFederated Learning	CodeCode Available	1
FedAdapter: Efficient Federated Learning for Modern NLP	May 20, 2022	Federated Learning	CodeCode Available	1
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings	Dec 26, 2021	AttributeEmotion Recognition	CodeCode Available	1
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning	Jul 9, 2020	FairnessFederated Learning	CodeCode Available	1
Bayesian Framework for Gradient Leakage	Nov 8, 2021	Federated Learning	CodeCode Available	1
Benchmarking Algorithms for Federated Domain Generalization	Jul 11, 2023	BenchmarkingDiversity	CodeCode Available	1
Benchmarking Differential Privacy and Federated Learning for BERT Models	Jun 26, 2021	BenchmarkingFederated Learning	CodeCode Available	1
Adaptive Federated Optimization	Feb 29, 2020	Federated Learning	CodeCode Available	1
Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing	May 29, 2024	Edge-computingFederated Learning	CodeCode Available	1
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging	Jul 10, 2020	Computed Tomography (CT)Federated Learning	CodeCode Available	1
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration	Sep 20, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees	May 24, 2022	Federated Learning	CodeCode Available	1
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing	Jun 16, 2020	Distributed OptimizationFederated Learning	CodeCode Available	1
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage	Mar 29, 2022	Bayesian OptimizationFederated Learning	CodeCode Available	1
CAFE: Catastrophic Data Leakage in Vertical Federated Learning	Oct 26, 2021	Federated LearningVertical Federated Learning	CodeCode Available	1
Can Textual Gradient Work in Federated Learning?	Feb 27, 2025	Federated Learning	CodeCode Available	1
CaPC Learning: Confidential and Private Collaborative Learning	Feb 9, 2021	FairnessFederated Learning	CodeCode Available	1
Catastrophic Data Leakage in Vertical Federated Learning	Dec 1, 2021	Federated LearningVertical Federated Learning	CodeCode Available	1
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction	Aug 29, 2023	Federated Learningimage-classification	CodeCode Available	1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning	Aug 23, 2021	Federated LearningMisconceptions	CodeCode Available	1
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift	Oct 24, 2024	ClusteringFederated Learning	CodeCode Available	1
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off	Feb 10, 2024	Federated Learning	CodeCode Available	1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data	Dec 14, 2023	Contrastive LearningFederated Learning	CodeCode Available	1
Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning	Apr 26, 2024	Backdoor AttackFederated Learning	CodeCode Available	1

Show:10 25 50

← PrevPage 7 of 271Next →

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