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 6351–6375 of 6771 papers

Title	Date	Tasks	Status	Hype
Backdoor attacks and defenses in feature-partitioned collaborative learning	Jul 7, 2020	Federated Learning	—Unverified	0
Defending against Backdoors in Federated Learning with Robust Learning Rate	Jul 7, 2020	Backdoor AttackFederated Learning	CodeCode Available	1
Sharing Models or Coresets: A Study based on Membership Inference Attack	Jul 6, 2020	Federated LearningInference Attack	—Unverified	0
Experiments of Federated Learning for COVID-19 Chest X-ray Images	Jul 5, 2020	Federated Learning	—Unverified	0
Delay Minimization for Federated Learning Over Wireless Communication Networks	Jul 5, 2020	Federated Learning	—Unverified	0
Multi-Armed Bandit Based Client Scheduling for Federated Learning	Jul 5, 2020	Federated LearningScheduling	CodeCode Available	1
Privacy Threats Against Federated Matrix Factorization	Jul 3, 2020	Collaborative FilteringFederated Learning	—Unverified	0
Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning	Jul 3, 2020	Federated LearningPrivacy Preserving	—Unverified	0
On the Outsized Importance of Learning Rates in Local Update Methods	Jul 2, 2020	Federated LearningMeta-Learning	CodeCode Available	0
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy	Jul 2, 2020	BIG-bench Machine LearningFederated Learning	—Unverified	0
Federated Learning with Compression: Unified Analysis and Sharp Guarantees	Jul 2, 2020	Distributed OptimizationFederated Learning	—Unverified	0
Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT	Jul 1, 2020	Edge-computingFederated Learning	CodeCode Available	1
FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications	Jun 28, 2020	Federated Learning	—Unverified	0
Federated Mutual Learning	Jun 27, 2020	Federated Learning	CodeCode Available	1
Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees	Jun 25, 2020	Federated Learning	CodeCode Available	0
Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms	Jun 24, 2020	Federated Learningreinforcement-learning	—Unverified	0
Security and Privacy Preserving Deep Learning	Jun 23, 2020	BIG-bench Machine LearningDeep Learning	—Unverified	0
Byzantine-Resilient High-Dimensional Federated Learning	Jun 22, 2020	Federated LearningVocal Bursts Intensity Prediction	—Unverified	0
D2P-Fed: Differentially Private Federated Learning With Efficient Communication	Jun 22, 2020	Federated Learning	—Unverified	0
Exact Support Recovery in Federated Regression with One-shot Communication	Jun 22, 2020	Distributed ComputingFederated Learning	—Unverified	0
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning	Jun 22, 2020	Federated Learning	CodeCode Available	1
Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC	Jun 21, 2020	BIG-bench Machine LearningFederated Learning	—Unverified	0
Free-rider Attacks on Model Aggregation in Federated Learning	Jun 21, 2020	Federated Learning	CodeCode Available	1
Federated Learning Meets Multi-objective Optimization	Jun 20, 2020	FairnessFederated Learning	CodeCode Available	1
DEED: A General Quantization Scheme for Communication Efficiency in Bits	Jun 19, 2020	Distributed OptimizationFederated Learning	—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