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 2626–2650 of 6771 papers

Title	Date	Tasks	Status	Hype
Multi-dimensional Fair Federated Learning	Dec 9, 2023	FairnessFederated Learning	—Unverified	0
Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approach	Dec 9, 2023	Federated LearningImage Classification	CodeCode Available	0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation	Dec 8, 2023	Active LearningDomain Adaptation	—Unverified	0
PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark	Dec 8, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	4
Topology-Based Reconstruction Prevention for Decentralised Learning	Dec 8, 2023	Federated LearningPrivacy Preserving	—Unverified	0
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security	Dec 7, 2023	Federated LearningIntrusion Detection	—Unverified	0
Improving Communication Efficiency of Federated Distillation via Accumulating Local Updates	Dec 7, 2023	Federated Learning	CodeCode Available	0
Coordination-free Decentralised Federated Learning on Complex Networks: Overcoming Heterogeneity	Dec 7, 2023	Edge-computingFederated Learning	—Unverified	0
Privacy-preserving quantum federated learning via gradient hiding	Dec 7, 2023	Distributed ComputingFederated Learning	—Unverified	0
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning	Dec 7, 2023	Federated Learningimage-classification	—Unverified	0
Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights	Dec 7, 2023	Federated Learning	—Unverified	0
Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data	Dec 7, 2023	Federated LearningPersonalized Federated Learning	—Unverified	0
FedGeo: Privacy-Preserving User Next Location Prediction with Federated Learning	Dec 6, 2023	Federated LearningPrivacy Preserving	—Unverified	0
TrustFed: A Reliable Federated Learning Framework with Malicious-Attack Resistance	Dec 6, 2023	Decision MakingDeep Reinforcement Learning	—Unverified	0
A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systems	Dec 6, 2023	Dimensionality ReductionFederated Learning	—Unverified	0
PCDP-SGD: Improving the Convergence of Differentially Private SGD via Projection in Advance	Dec 6, 2023	Federated Learning	—Unverified	0
Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service	Dec 5, 2023	FairnessFederated Learning	—Unverified	0
The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning	Dec 5, 2023	Deep LearningDistributed Computing	—Unverified	0
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts	Dec 5, 2023	Active LearningDiversity	CodeCode Available	1
Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor	Dec 5, 2023	Federated Learning	CodeCode Available	0
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks	Dec 4, 2023	Data PoisoningFederated Learning	—Unverified	0
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation	Dec 4, 2023	Federated LearningPrivacy Preserving	CodeCode Available	0
Mitigating Data Injection Attacks on Federated Learning	Dec 4, 2023	Federated Learning	—Unverified	0
Federated Active Learning for Target Domain Generalisation	Dec 4, 2023	Active LearningFederated Learning	CodeCode Available	0
Federated Learning is Better with Non-Homomorphic Encryption	Dec 4, 2023	Federated LearningPrivacy Preserving	—Unverified	0

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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