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 5001–5025 of 6771 papers

Title	Date	Tasks	Status	Hype
Federated Learning Challenges and Opportunities: An Outlook	Feb 1, 2022	Federated LearningLifelong learning	—Unverified	0
Factorized-FL: Agnostic Personalized Federated Learning with Kernel Factorization & Similarity Matching	Feb 1, 2022	Federated LearningPersonalized Federated Learning	—Unverified	0
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?	Feb 1, 2022	Distributed OptimizationFederated Learning	CodeCode Available	1
Personalized Federated Learning via Convex Clustering	Feb 1, 2022	ClusteringFederated Learning	—Unverified	0
Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning	Feb 1, 2022	Active LearningDistributed Optimization	—Unverified	0
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification	Feb 1, 2022	Federated Learning	—Unverified	0
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors	Jan 31, 2022	Anomaly DetectionBinary Classification	—Unverified	0
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training	Jan 31, 2022	Federated LearningPrivacy Preserving	—Unverified	0
Federated Learning with Erroneous Communication Links	Jan 31, 2022	Federated Learning	—Unverified	0
DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning	Jan 30, 2022	Deep Reinforcement LearningFederated Learning	—Unverified	0
Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning	Jan 30, 2022	Decision MakingFederated Learning	—Unverified	0
Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications	Jan 29, 2022	Federated Learning	—Unverified	0
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models	Jan 29, 2022	Federated Learning	—Unverified	0
Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters	Jan 29, 2022	Face RecognitionFederated Learning	—Unverified	0
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks	Jan 28, 2022	Federated LearningNode Classification	CodeCode Available	1
A Secure and Efficient Federated Learning Framework for NLP	Jan 28, 2022	Federated Learning	—Unverified	0
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients	Jan 28, 2022	Federated Learning	—Unverified	0
Gradient Masked Averaging for Federated Learning	Jan 28, 2022	Federated LearningOut-of-Distribution Generalization	—Unverified	0
On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning	Jan 27, 2022	Federated LearningOpen-Ended Question Answering	—Unverified	0
Towards a Secure and Reliable Federated Learning using Blockchain	Jan 27, 2022	Federated Learning	—Unverified	0
Achieving Personalized Federated Learning with Sparse Local Models	Jan 27, 2022	Federated LearningPersonalized Federated Learning	—Unverified	0
A dual approach for federated learning	Jan 26, 2022	Federated Learning	CodeCode Available	0
Fast Server Learning Rate Tuning for Coded Federated Dropout	Jan 26, 2022	Federated Learning	—Unverified	0
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization	Jan 26, 2022	Federated Learning	—Unverified	0
Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients	Jan 26, 2022	Federated LearningImage Classification	CodeCode Available	1

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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