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 1776–1800 of 6771 papers

Title	Date	Tasks	Status	Hype
Federated Learning with Flexible Architectures	Jun 14, 2024	Backdoor AttackFederated Learning	—Unverified	0
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation	Jun 13, 2024	Federated LearningPrivacy Preserving	CodeCode Available	0
EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters	Jun 13, 2024	Federated LearningModel Compression	—Unverified	0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation	Jun 12, 2024	Federated LearningMulti-Armed Bandits	—Unverified	0
IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content	Jun 12, 2024	Federated Learning	—Unverified	0
Regularizing and Aggregating Clients with Class Distribution for Personalized Federated Learning	Jun 12, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	0
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data	Jun 12, 2024	Computational EfficiencyFederated Learning	—Unverified	0
A deep cut into Split Federated Self-supervised Learning	Jun 12, 2024	Federated LearningSelf-Supervised Learning	CodeCode Available	0
Minimizing Energy Costs in Deep Learning Model Training: The Gaussian Sampling Approach	Jun 11, 2024	Domain AdaptationDomain Generalization	—Unverified	0
Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests	Jun 10, 2024	Federated Learning	—Unverified	0
Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm	Jun 10, 2024	Federated LearningSecond-order methods	—Unverified	0
Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints	Jun 10, 2024	Federated LearningPrivacy Preserving	—Unverified	0
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning	Jun 10, 2024	Backdoor AttackFederated Learning	—Unverified	0
Federated learning in food research	Jun 10, 2024	Federated LearningFraud Detection	—Unverified	0
Decentralized Personalized Federated Learning	Jun 10, 2024	Federated LearningPersonalized Federated Learning	—Unverified	0
Optimisation of federated learning settings under statistical heterogeneity variations	Jun 10, 2024	DiversityFederated Learning	—Unverified	0
PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System	Jun 8, 2024	Federated LearningLanguage Modelling	CodeCode Available	0
Federated LoRA with Sparse Communication	Jun 7, 2024	Federated Learning	CodeCode Available	0
When Swarm Learning meets energy series data: A decentralized collaborative learning design based on blockchain	Jun 7, 2024	Federated Learning	—Unverified	0
Federated Representation Learning in the Under-Parameterized Regime	Jun 7, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	0
FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models	Jun 7, 2024	Federated Learning	CodeCode Available	3
1-D CNN-Based Online Signature Verification with Federated Learning	Jun 6, 2024	Federated Learning	—Unverified	0
R-CONV: An Analytical Approach for Efficient Data Reconstruction via Convolutional Gradients	Jun 6, 2024	Federated Learning	CodeCode Available	0
Buffered Asynchronous Secure Aggregation for Cross-Device Federated Learning	Jun 5, 2024	Federated Learning	—Unverified	0
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning	Jun 5, 2024	Federated Learning	CodeCode Available	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