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 5776–5800 of 6771 papers

Title	Date	Tasks	Status
Sensing and Mapping for Better Roads: Initial Plan for Using Federated Learning and Implementing a Digital Twin to Identify the Road Conditions in a Developing Country -- Sri Lanka	Jul 30, 2021	Edge-computingFederated Learning	—Unverified
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness	Jul 30, 2021	Distributed ComputingEdge-computing	—Unverified
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges	Jul 30, 2021	Distributed ComputingFederated Learning	—Unverified
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning	Jul 29, 2021	Federated LearningKnowledge Distillation	—Unverified
HAFLO: GPU-Based Acceleration for Federated Logistic Regression	Jul 29, 2021	Federated LearningGPU	—Unverified
New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning	Jul 28, 2021	FairnessFederated Learning	—Unverified
Federated Learning Meets Natural Language Processing: A Survey	Jul 27, 2021	Federated LearningSurvey	—Unverified
A Payload Optimization Method for Federated Recommender Systems	Jul 27, 2021	Federated LearningRecommendation Systems	—Unverified
Towards Industrial Private AI: A two-tier framework for data and model security	Jul 27, 2021	Federated Learning	—Unverified
Preliminary Steps Towards Federated Sentiment Classification	Jul 26, 2021	ClassificationDimensionality Reduction	—Unverified
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes	Jul 26, 2021	Federated Learning	—Unverified
LEGATO: A LayerwisE Gradient AggregaTiOn Algorithm for Mitigating Byzantine Attacks in Federated Learning	Jul 26, 2021	Federated Learning	—Unverified
Accelerated Gradient Descent Learning over Multiple Access Fading Channels	Jul 26, 2021	Federated Learning	—Unverified
Federated Learning with Fair Worker Selection: A Multi-Round Submodular Maximization Approach	Jul 25, 2021	FairnessFederated Learning	—Unverified
Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application	Jul 24, 2021	Federated LearningMulti-Task Learning	CodeCode Available
Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning	Jul 23, 2021	Federated LearningScheduling	—Unverified
Communication Efficiency in Federated Learning: Achievements and Challenges	Jul 23, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Federated Learning Versus Classical Machine Learning: A Convergence Comparison	Jul 22, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning	Jul 21, 2021	Federated Learning	CodeCode Available
Defending against Reconstruction Attack in Vertical Federated Learning	Jul 21, 2021	Federated LearningPrivacy Preserving	—Unverified
How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?	Jul 20, 2021	Federated Learning	—Unverified
Precision-Weighted Federated Learning	Jul 20, 2021	Computational EfficiencyFederated Learning	—Unverified
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions	Jul 20, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression	Jul 20, 2021	Distributed OptimizationFederated Learning	—Unverified
Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach	Jul 19, 2021	Federated 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