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 6001–6025 of 6771 papers

Title	Date	Tasks	Status
On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI	Apr 1, 2021	Federated Learning	—Unverified
Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space	Apr 1, 2021	Federated LearningKnowledge Distillation	—Unverified
Federated Few-Shot Learning with Adversarial Learning	Apr 1, 2021	Federated LearningFew-Shot Learning	—Unverified
Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theory	Mar 31, 2021	Federated Learning	—Unverified
Federated Learning: A Signal Processing Perspective	Mar 31, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing	Mar 30, 2021	Federated Learning	—Unverified
User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative environments	Mar 30, 2021	Emotion RecognitionFederated Learning	—Unverified
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air	Mar 30, 2021	Compressive SensingDimensionality Reduction	—Unverified
Privacy and Trust Redefined in Federated Machine Learning	Mar 29, 2021	BIG-bench Machine LearningFederated Learning	CodeCode Available
Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design	Mar 26, 2021	Federated LearningQuantization	—Unverified
Prior-Independent Auctions for the Demand Side of Federated Learning	Mar 26, 2021	Federated Learning	—Unverified
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning	Mar 24, 2021	Federated Learning	—Unverified
Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications	Mar 24, 2021	Federated Learning	—Unverified
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication	Mar 24, 2021	Decision MakingDeep Reinforcement Learning	—Unverified
Real-time End-to-End Federated Learning: An Automotive Case Study	Mar 22, 2021	Autonomous DrivingBIG-bench Machine Learning	—Unverified
A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning	Mar 22, 2021	Federated LearningPrivacy Preserving	—Unverified
Server Averaging for Federated Learning	Mar 22, 2021	Federated Learning	—Unverified
Federated Quantum Machine Learning	Mar 22, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
UAV Communications for Sustainable Federated Learning	Mar 20, 2021	Federated Learning	—Unverified
Demystifying the Effects of Non-Independence in Federated Learning	Mar 20, 2021	FairnessFederated Learning	—Unverified
Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations	Mar 18, 2021	Federated Learning	CodeCode Available
Escaping Saddle Points in Distributed Newton's Method with Communication Efficiency and Byzantine Resilience	Mar 17, 2021	Federated Learning	—Unverified
Sample-based Federated Learning via Mini-batch SSCA	Mar 17, 2021	Federated LearningPrivacy Preserving	—Unverified
Quantum federated learning through blind quantum computing	Mar 15, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Simeon -- Secure Federated Machine Learning Through Iterative Filtering	Mar 13, 2021	BIG-bench Machine LearningFederated 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