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 6276–6300 of 6771 papers

Title	Date	Tasks	Status
Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms	Oct 25, 2020	Distributed OptimizationFederated Learning	—Unverified
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things	Oct 25, 2020	ClusteringDeep Reinforcement Learning	—Unverified
Federated Bandit: A Gossiping Approach	Oct 24, 2020	Federated Learning	—Unverified
Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD	Oct 24, 2020	Federated Learning	CodeCode Available
Differentially-Private Federated Linear Bandits	Oct 22, 2020	Federated Learning	CodeCode Available
Hierarchical Federated Learning through LAN-WAN Orchestration	Oct 22, 2020	Federated Learning	—Unverified
Network Anomaly Detection Using Federated Learning and Transfer Learning	Oct 22, 2020	Anomaly DetectionFederated Learning	—Unverified
GFL: A Decentralized Federated Learning Framework Based On Blockchain	Oct 21, 2020	Data PoisoningFederated Learning	—Unverified
Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation	Oct 20, 2020	Federated LearningKnowledge Distillation	—Unverified
A Federated Learning Approach to Anomaly Detection in Smart Buildings	Oct 20, 2020	Anomaly DetectionFederated Learning	—Unverified
Federated Unsupervised Representation Learning	Oct 18, 2020	Federated LearningRepresentation Learning	—Unverified
Sliding Differential Evolution Scheduling for Federated Learning in Bandwidth-Limited Networks	Oct 18, 2020	Federated LearningScheduling	—Unverified
Layer-wise Characterization of Latent Information Leakage in Federated Learning	Oct 17, 2020	Federated Learning	—Unverified
Flow-FL: Data-Driven Federated Learning for Spatio-Temporal Predictions in Multi-Robot Systems	Oct 16, 2020	Federated LearningTrajectory Forecasting	—Unverified
Federated Learning in Adversarial Settings	Oct 15, 2020	Federated LearningQuantization	—Unverified
BlockFLA: Accountable Federated Learning via Hybrid Blockchain Architecture	Oct 14, 2020	Federated Learning	—Unverified
Improving Accuracy of Federated Learning in Non-IID Settings	Oct 14, 2020	Federated Learning	—Unverified
COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework	Oct 13, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Can Federated Learning Save The Planet?	Oct 13, 2020	Federated Learning	—Unverified
Direct Federated Neural Architecture Search	Oct 13, 2020	Federated LearningNeural Architecture Search	—Unverified
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications	Oct 12, 2020	Federated LearningFraud Detection	—Unverified
FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers	Oct 12, 2020	Federated Learning	—Unverified
Fairness-aware Agnostic Federated Learning	Oct 10, 2020	FairnessFederated Learning	—Unverified
Voting-based Approaches For Differentially Private Federated Learning	Oct 9, 2020	Federated LearningTransfer Learning	—Unverified
Optimal Gradient Compression for Distributed and Federated Learning	Oct 7, 2020	Federated LearningQuantization	—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