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
PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications	Mar 14, 2025	Federated Learning	—Unverified	0
Federated Koopman-Reservoir Learning for Large-Scale Multivariate Time-Series Anomaly Detection	Mar 14, 2025	Anomaly DetectionFederated Learning	—Unverified	0
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration	Mar 14, 2025	Federated LearningSecond-order methods	—Unverified	0
Byzantine-Resilient Federated Learning via Distributed Optimization	Mar 13, 2025	Distributed OptimizationFederated Learning	—Unverified	0
PluralLLM: Pluralistic Alignment in LLMs via Federated Learning	Mar 13, 2025	FairnessFederated Learning	—Unverified	0
FedPCA: Noise-Robust Fair Federated Learning via Performance-Capacity Analysis	Mar 13, 2025	AttributeFairness	—Unverified	0
A Multi-Modal Federated Learning Framework for Remote Sensing Image Classification	Mar 13, 2025	Federated Learningimage-classification	—Unverified	0
dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis	Mar 13, 2025	Federated LearningMixture-of-Experts	—Unverified	0
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks	Mar 13, 2025	Federated LearningPrivacy Preserving	CodeCode Available	1
One-Shot Federated Unsupervised Domain Adaptation with Scaled Entropy Attention and Multi-Source Smoothed Pseudo Labeling	Mar 13, 2025	Domain AdaptationFederated Learning	—Unverified	0
Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models	Mar 13, 2025	Federated Learning	—Unverified	0
Privacy-Preserved Automated Scoring using Federated Learning for Educational Research	Mar 12, 2025	Federated Learningparameter-efficient fine-tuning	CodeCode Available	0
A Comprehensive Review on Understanding the Decentralized and Collaborative Approach in Machine Learning	Mar 12, 2025	Federated Learning	—Unverified	0
Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients	Mar 12, 2025	Contrastive LearningData Augmentation	CodeCode Available	0
Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing	Mar 12, 2025	Federated Learning	—Unverified	0
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning	Mar 12, 2025	Federated LearningMULTI-VIEW LEARNING	—Unverified	0
Technical Insights and Legal Considerations for Advancing Federated Learning in Bioinformatics	Mar 12, 2025	Federated LearningTranslation	CodeCode Available	0
Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning	Mar 12, 2025	AllFederated Learning	—Unverified	0
Mitigating Membership Inference Vulnerability in Personalized Federated Learning	Mar 12, 2025	ClusteringFairness	—Unverified	0
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates	Mar 12, 2025	Federated LearningPersonalized Federated Learning	—Unverified	0
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching	Mar 12, 2025	FairnessFederated Learning	CodeCode Available	1
Drift-Aware Federated Learning: A Causal Perspective	Mar 12, 2025	Federated Learning	—Unverified	0
Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data	Mar 11, 2025	Federated Learning	—Unverified	0
Scaling Probabilistic Circuits via Data Partitioning	Mar 11, 2025	Density EstimationFederated Learning	CodeCode Available	0
Smoothing ADMM for Non-convex and Non-smooth Hierarchical Federated Learning	Mar 11, 2025	Federated Learning	—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