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 2226–2250 of 6771 papers

Title	Date	Tasks	Status	Hype
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains	Mar 11, 2024	Federated LearningStochastic Optimization	—Unverified	0
Adaptive Federated Learning Over the Air	Mar 11, 2024	Federated Learning	—Unverified	0
DrJAX: Scalable and Differentiable MapReduce Primitives in JAX	Mar 11, 2024	Federated Learning	—Unverified	0
Analysis of Total Variation Minimization for Clustered Federated Learning	Mar 10, 2024	ClusteringFederated Learning	—Unverified	0
Fake or Compromised? Making Sense of Malicious Clients in Federated Learning	Mar 10, 2024	Federated Learning	—Unverified	0
FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning	Mar 10, 2024	Federated LearningFew-Shot Learning	—Unverified	0
Fluent: Round-efficient Secure Aggregation for Private Federated Learning	Mar 10, 2024	Federated Learning	—Unverified	0
Towards Efficient Replay in Federated Incremental Learning	Mar 9, 2024	Federated LearningIncremental Learning	—Unverified	0
RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility	Mar 8, 2024	Federated LearningMULTI-VIEW LEARNING	—Unverified	0
FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation	Mar 8, 2024	Federated LearningImage Segmentation	CodeCode Available	2
Federated Learning Method for Preserving Privacy in Face Recognition System	Mar 8, 2024	Face RecognitionFederated Learning	—Unverified	0
Boosting Fairness and Robustness in Over-the-Air Federated Learning	Mar 7, 2024	FairnessFederated Learning	—Unverified	0
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression	Mar 7, 2024	Distributed OptimizationFederated Learning	—Unverified	0
HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning	Mar 7, 2024	Domain GeneralizationFairness	CodeCode Available	0
FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning	Mar 7, 2024	Federated Learning	—Unverified	0
On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks	Mar 7, 2024	DiversityFederated Learning	—Unverified	0
Architectural Blueprint For Heterogeneity-Resilient Federated Learning	Mar 7, 2024	Edge-computingFederated Learning	—Unverified	0
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering	Mar 7, 2024	Federated Learning	—Unverified	0
Many-Objective Multi-Solution Transport	Mar 6, 2024	Federated LearningMulti-Task Learning	—Unverified	0
OCD-FL: A Novel Communication-Efficient Peer Selection-based Decentralized Federated Learning	Mar 6, 2024	Federated Learning	—Unverified	0
SPEAR:Exact Gradient Inversion of Batches in Federated Learning	Mar 6, 2024	Federated LearningGPU	—Unverified	0
Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem	Mar 6, 2024	Federated Learning	—Unverified	0
Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Data	Mar 6, 2024	Federated LearningVertical Federated Learning	—Unverified	0
Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation	Mar 5, 2024	Anomaly DetectionComputational Efficiency	—Unverified	0
Leveraging Federated Learning for Automatic Detection of Clopidogrel Treatment Failures	Mar 5, 2024	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