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 4001–4025 of 6771 papers

Title	Date	Tasks	Status
Project Florida: Federated Learning Made Easy	Jul 21, 2023	Federated LearningManagement	—Unverified
Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization	Sep 18, 2024	Code GenerationFederated Learning	—Unverified
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA	Jun 16, 2024	Federated Learningparameter-efficient fine-tuning	—Unverified
Prompt-based Personalized Federated Learning for Medical Visual Question Answering	Feb 15, 2024	Federated LearningMedical Visual Question Answering	—Unverified
PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models -- Federated Learning in Age of Foundation Model	Aug 24, 2022	Federated Learning	—Unverified
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications	Apr 5, 2024	Federated Learning	—Unverified
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain	Feb 27, 2023	Contribution AssessmentFederated Learning	—Unverified
Proof-of-Data: A Consensus Protocol for Collaborative Intelligence	Jan 6, 2025	FairnessFederated Learning	—Unverified
Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain Based on Federated Learning	Jul 30, 2023	Deep LearningFederated Learning	—Unverified
Proof of Swarm Based Ensemble Learning for Federated Learning Applications	Dec 28, 2022	ECG ClassificationEnsemble Learning	—Unverified
Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases	Feb 3, 2020	Deep Reinforcement LearningFederated Learning	—Unverified
Protea: Client Profiling within Federated Systems using Flower	Jul 3, 2022	Federated LearningGPU	—Unverified
Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation	Aug 22, 2023	Backdoor AttackFederated Learning	—Unverified
Protecting Data from all Parties: Combining FHE and DP in Federated Learning	May 9, 2022	AllFederated Learning	—Unverified
Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning	Oct 9, 2023	Federated Learning	—Unverified
Protection Against Reconstruction and Its Applications in Private Federated Learning	Dec 3, 2018	Federated Learningimage-classification	—Unverified
ProtoFL: Unsupervised Federated Learning via Prototypical Distillation	Jul 23, 2023	Federated LearningOne-Class Classification	—Unverified
Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data	Mar 11, 2025	Federated Learning	—Unverified
Prototype-Based Layered Federated Cross-Modal Hashing	Oct 27, 2022	Federated LearningPersonalized Federated Learning	—Unverified
Prototype Guided Federated Learning of Visual Feature Representations	May 19, 2021	Federated Learningimage-classification	—Unverified
Prototype Helps Federated Learning: Towards Faster Convergence	Mar 22, 2023	Federated Learning	—Unverified
Prototype of deployment of Federated Learning with IoT devices	Nov 24, 2023	Federated Learning	—Unverified
Provable Federated Adversarial Learning via Min-max Optimization	Sep 29, 2021	Federated Learning	—Unverified
Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains	Mar 11, 2024	Federated LearningStochastic Optimization	—Unverified
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization	Jul 12, 2024	Distributed OptimizationFederated Learning	—Unverified

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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