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 1501–1525 of 6771 papers

Title	Date	Tasks	Status
DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences	Feb 7, 2025	Federated Learningmodel	—Unverified
Using Federated Machine Learning in Predictive Maintenance of Jet Engines	Feb 7, 2025	Federated Learning	—Unverified
Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks	Feb 6, 2025	Federated LearningTransfer Learning	—Unverified
Private Federated Learning In Real World Application -- A Case Study	Feb 6, 2025	Federated Learning	—Unverified
Comparing privacy notions for protection against reconstruction attacks in machine learning	Feb 6, 2025	Federated Learning	—Unverified
Non-convex composite federated learning with heterogeneous data	Feb 6, 2025	Federated Learning	—Unverified
Vertical Federated Learning for Failure-Cause Identification in Disaggregated Microwave Networks	Feb 5, 2025	Federated LearningManagement	—Unverified
Interaction-Aware Gaussian Weighting for Clustered Federated Learning	Feb 5, 2025	ClusteringFederated Learning	—Unverified
The Other Side of the Coin: Unveiling the Downsides of Model Aggregation in Federated Learning from a Layer-peeled Perspective	Feb 5, 2025	Federated Learning	—Unverified
E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing	Feb 5, 2025	Federated LearningScheduling	CodeCode Available
FedP^2EFT: Federated Learning to Personalize Parameter Efficient Fine-Tuning for Multilingual LLMs	Feb 5, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified
Multi-objective methods in Federated Learning: A survey and taxonomy	Feb 5, 2025	FairnessFederated Learning	—Unverified
A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large Language Models Deployment in Edge-Cloud-based Federated Learning Environments	Feb 4, 2025	Cloud ComputingFederated Learning	—Unverified
Gradient Correction in Federated Learning with Adaptive Optimization	Feb 4, 2025	Federated Learning	—Unverified
Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction	Feb 4, 2025	Federated LearningImage Reconstruction	—Unverified
FedGES: A Federated Learning Approach for BN Structure Learning	Feb 3, 2025	Federated LearningPrivacy Preserving	—Unverified
Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks	Feb 3, 2025	Federated LearningInference Attack	—Unverified
Federated Learning with Discriminative Naive Bayes Classifier	Feb 3, 2025	Federated Learning	—Unverified
Decoding FL Defenses: Systemization, Pitfalls, and Remedies	Feb 3, 2025	Federated LearningSurvey	—Unverified
A Framework for Double-Blind Federated Adaptation of Foundation Models	Feb 3, 2025	Federated Learningimage-classification	—Unverified
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis	Feb 3, 2025	Contrastive LearningDisentanglement	—Unverified
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework	Feb 2, 2025	Federated LearningUncertainty Quantification	—Unverified
FedRIR: Rethinking Information Representation in Federated Learning	Feb 2, 2025	Federated Learning	CodeCode Available
Data Overvaluation Attack and Truthful Data Valuation in Federated Learning	Feb 1, 2025	Data ValuationFederated Learning	—Unverified
Robust Knowledge Distillation in Federated Learning: Counteracting Backdoor Attacks	Feb 1, 2025	Federated LearningKnowledge Distillation	CodeCode Available

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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