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 6376–6400 of 6771 papers

Title	Date	Tasks	Status
Holdout SGD: Byzantine Tolerant Federated Learning	Aug 11, 2020	Federated Learning	—Unverified
FedSKETCH: Communication-Efficient and Private Federated Learning via Sketching	Aug 11, 2020	Federated Learning	—Unverified
Improving on-device speaker verification using federated learning with privacy	Aug 6, 2020	Federated LearningMulti-Task Learning	—Unverified
Federated Transfer Learning with Dynamic Gradient Aggregation	Aug 6, 2020	Federated Learningspeech-recognition	—Unverified
E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI	Aug 4, 2020	ClusteringFederated Learning	—Unverified
The Need for Advanced Intelligence in NFV Management and Orchestration	Aug 3, 2020	Federated LearningManagement	—Unverified
Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning	Aug 3, 2020	DecoderDiversity	—Unverified
State-of-the-art Techniques in Deep Edge Intelligence	Aug 3, 2020	Deep LearningEdge-computing	—Unverified
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization	Aug 1, 2020	Federated Learning	—Unverified
LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy	Jul 31, 2020	Federated Learning	—Unverified
Communication-Efficient Federated Learning via Optimal Client Sampling	Jul 30, 2020	Federated LearningLanguage Modeling	—Unverified
Efficient Sparse Secure Aggregation for Federated Learning	Jul 29, 2020	Federated Learning	—Unverified
Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems	Jul 28, 2020	Edge-computingFederated Learning	—Unverified
Learning discrete distributions: user vs item-level privacy	Jul 27, 2020	Federated Learning	—Unverified
Evaluation of Federated Learning in Phishing Email Detection	Jul 27, 2020	Distributed ComputingFederated Learning	—Unverified
Fast-Convergent Federated Learning	Jul 26, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence	Jul 25, 2020	Federated LearningSelf-Supervised Learning	—Unverified
Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms	Jul 23, 2020	Federated Learning	—Unverified
A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective	Jul 22, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
FedOCR: Communication-Efficient Federated Learning for Scene Text Recognition	Jul 22, 2020	Federated LearningPrivacy Preserving	—Unverified
FPGA-Based Hardware Accelerator of Homomorphic Encryption for Efficient Federated Learning	Jul 21, 2020	Federated LearningHigh-Level Synthesis	—Unverified
Incentives for Federated Learning: a Hypothesis Elicitation Approach	Jul 21, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
Byzantine-Resilient Secure Federated Learning	Jul 21, 2020	Federated LearningOutlier Detection	—Unverified
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach	Jul 19, 2020	Anomaly DetectionFederated Learning	—Unverified
Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms	Jul 18, 2020	Federated LearningGeneralization Bounds	—Unverified

Show:10 25 50

← PrevPage 256 of 271Next →

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