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 5301–5325 of 6771 papers

Title	Date	Tasks	Status
Improved Information Theoretic Generalization Bounds for Distributed and Federated Learning	Feb 4, 2022	Federated LearningGeneralization Bounds	—Unverified
Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization	Feb 4, 2022	Federated Learning	—Unverified
Comparative assessment of federated and centralized machine learning	Feb 3, 2022	BIG-bench Machine LearningFederated Learning	—Unverified
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT	Feb 3, 2022	Deep Reinforcement LearningFederated Learning	—Unverified
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT	Feb 3, 2022	Federated Learning	CodeCode Available
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations	Feb 2, 2022	Federated Learning	—Unverified
Communication Efficient Federated Learning for Generalized Linear Bandits	Feb 2, 2022	Federated Learningparameter estimation	—Unverified
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization	Feb 2, 2022	Distributed OptimizationFederated Learning	—Unverified
Federated Reinforcement Learning for Collective Navigation of Robotic Swarms	Feb 2, 2022	Deep Reinforcement LearningFederated Learning	—Unverified
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning	Feb 1, 2022	Active LearningDistributed Optimization	—Unverified
Personalized Federated Learning via Convex Clustering	Feb 1, 2022	ClusteringFederated Learning	—Unverified
Factorized-FL: Agnostic Personalized Federated Learning with Kernel Factorization & Similarity Matching	Feb 1, 2022	Federated LearningPersonalized Federated Learning	—Unverified
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification	Feb 1, 2022	Federated Learning	—Unverified
Federated Learning Challenges and Opportunities: An Outlook	Feb 1, 2022	Federated LearningLifelong learning	—Unverified
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors	Jan 31, 2022	Anomaly DetectionBinary Classification	—Unverified
Federated Learning with Erroneous Communication Links	Jan 31, 2022	Federated Learning	—Unverified
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training	Jan 31, 2022	Federated LearningPrivacy Preserving	—Unverified
Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning	Jan 30, 2022	Decision MakingFederated Learning	—Unverified
DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning	Jan 30, 2022	Deep Reinforcement LearningFederated Learning	—Unverified
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models	Jan 29, 2022	Federated Learning	—Unverified
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters	Jan 29, 2022	Face RecognitionFederated Learning	—Unverified
Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications	Jan 29, 2022	Federated Learning	—Unverified
A Secure and Efficient Federated Learning Framework for NLP	Jan 28, 2022	Federated Learning	—Unverified
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients	Jan 28, 2022	Federated Learning	—Unverified
Gradient Masked Averaging for Federated Learning	Jan 28, 2022	Federated LearningOut-of-Distribution Generalization	—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