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 4976–5000 of 6771 papers

Title	Date	Tasks	Status	Hype
Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data	Feb 8, 2022	Federated LearningSurvival Analysis	—Unverified	0
Fabricated Flips: Poisoning Federated Learning without Data	Feb 7, 2022	Federated Learning	—Unverified	0
Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning	Feb 7, 2022	Federated LearningSurvey	—Unverified	0
Preserving Privacy and Security in Federated Learning	Feb 7, 2022	Federated Learning	—Unverified	0
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks	Feb 7, 2022	Federated LearningPrivacy Preserving	—Unverified	0
FL_PyTorch: optimization research simulator for federated learning	Feb 7, 2022	Federated LearningGPU	CodeCode Available	1
ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving Cooperative Learning	Feb 7, 2022	BIG-bench Machine LearningFederated Learning	—Unverified	0
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks	Feb 7, 2022	Federated Learning	—Unverified	0
Lossy Gradient Compression: How Much Accuracy Can One Bit Buy?	Feb 6, 2022	Federated Learning	CodeCode Available	0
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning	Feb 6, 2022	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	1
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning	Feb 6, 2022	Deep Reinforcement LearningFederated Learning	—Unverified	0
Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting	Feb 5, 2022	Distributed OptimizationFederated Learning	—Unverified	0
Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learning	Feb 5, 2022	Emotion RecognitionFederated Learning	CodeCode Available	1
A Coalition Formation Game Approach for Personalized Federated Learning	Feb 5, 2022	DiversityFederated Learning	—Unverified	0
Improved Information Theoretic Generalization Bounds for Distributed and Federated Learning	Feb 4, 2022	Federated LearningGeneralization Bounds	—Unverified	0
Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization	Feb 4, 2022	Federated Learning	—Unverified	0
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT	Feb 3, 2022	Deep Reinforcement LearningFederated Learning	—Unverified	0
Comparative assessment of federated and centralized machine learning	Feb 3, 2022	BIG-bench Machine LearningFederated Learning	—Unverified	0
Proportional Fairness in Federated Learning	Feb 3, 2022	FairnessFederated Learning	CodeCode Available	1
Byzantine-Robust Decentralized Learning via ClippedGossip	Feb 3, 2022	Federated Learning	CodeCode Available	1
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT	Feb 3, 2022	Federated Learning	CodeCode Available	0
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations	Feb 2, 2022	Federated Learning	—Unverified	0
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization	Feb 2, 2022	Distributed OptimizationFederated Learning	—Unverified	0
Federated Reinforcement Learning for Collective Navigation of Robotic Swarms	Feb 2, 2022	Deep Reinforcement LearningFederated Learning	—Unverified	0
Communication Efficient Federated Learning for Generalized Linear Bandits	Feb 2, 2022	Federated Learningparameter estimation	—Unverified	0

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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