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 701–725 of 6771 papers

Title	Date	Tasks	Status	Hype	Score
Locally Adaptive Federated Learning	Jul 12, 2023	Federated Learning	CodeCode Available	1	5
Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization	May 29, 2024	Federated Learning	CodeCode Available	1	5
Decentralized Federated Learning: A Segmented Gossip Approach	Aug 21, 2019	Federated Learning	CodeCode Available	1	5
Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT	Feb 15, 2022	Deep Reinforcement LearningEdge-computing	CodeCode Available	1	5
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction	Sep 12, 2019	Distributed OptimizationFederated Learning	CodeCode Available	1	5
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification	Sep 13, 2019	Federated LearningGeneral Classification	CodeCode Available	1	5
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks	Mar 13, 2025	Federated LearningPrivacy Preserving	CodeCode Available	1	5
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning	Aug 8, 2020	Federated Learning	CodeCode Available	1	5
Agnostic Federated Learning	Feb 1, 2019	Cloud ComputingDomain Adaptation	CodeCode Available	1	5
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation	Apr 14, 2022	Federated LearningPrivacy Preserving	CodeCode Available	1	5
FedAdapter: Efficient Federated Learning for Modern NLP	May 20, 2022	Federated Learning	CodeCode Available	1	5
Acceleration of Federated Learning with Alleviated Forgetting in Local Training	Mar 5, 2022	Distributed OptimizationFederated Learning	CodeCode Available	1	5
Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning	Aug 19, 2021	AllFairness	CodeCode Available	1	5
mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup	Apr 7, 2025	Domain AdaptationEEG	CodeCode Available	1	5
Practical One-Shot Federated Learning for Cross-Silo Setting	Oct 2, 2020	Federated LearningTransfer Learning	CodeCode Available	1	5
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks	Jun 3, 2023	Federated LearningMulti-agent Reinforcement Learning	CodeCode Available	1	5
Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity	May 26, 2024	DiversityFairness	CodeCode Available	1	5
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State Matching	Mar 12, 2025	FairnessFederated Learning	CodeCode Available	1	5
Fast Optimal Locally Private Mean Estimation via Random Projections	Jun 7, 2023	Federated Learning	CodeCode Available	1	5
Feature Inference Attack on Model Predictions in Vertical Federated Learning	Oct 20, 2020	Federated LearningInference Attack	CodeCode Available	1	5
Fast Federated Learning by Balancing Communication Trade-Offs	May 23, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1	5
Fast Federated Learning in the Presence of Arbitrary Device Unavailability	Jun 8, 2021	Federated Learning	CodeCode Available	1	5
Federated Mutual Learning	Jun 27, 2020	Federated Learning	CodeCode Available	1	5
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee	Oct 26, 2021	Decision MakingFederated Learning	CodeCode Available	1	5
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression	Jun 14, 2022	Federated LearningQuantization	CodeCode Available	1	5

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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