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 5876–5900 of 6771 papers

Title	Date	Tasks	Status
Communication-Efficient and Personalized Federated Lottery Ticket Learning	Apr 26, 2021	Federated LearningMulti-Task Learning	—Unverified
Communication-Efficient and Personalized Federated Foundation Model Fine-Tuning via Tri-Matrix Adaptation	Mar 31, 2025	Federated Learning	—Unverified
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models	Oct 16, 2024	DecoderFederated Learning	—Unverified
Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization	Jun 20, 2024	Federated Learning	—Unverified
Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach	Feb 28, 2024	Federated Learning	—Unverified
Communication-Efficient Consensus Mechanism for Federated Reinforcement Learning	Jan 30, 2022	Decision MakingFederated Learning	—Unverified
Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection	Feb 22, 2020	Federated Learning	—Unverified
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing	Aug 31, 2023	Compressive SensingComputational Efficiency	—Unverified
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization	Jan 19, 2022	Federated LearningPrivacy Preserving	—Unverified
Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization	Mar 1, 2025	Federated LearningScheduling	—Unverified
Communication Efficient, Differentially Private Distributed Optimization using Correlation-Aware Sketching	Jul 4, 2025	Distributed OptimizationFederated Learning	—Unverified
Communication-Efficient Distributed SGD with Compressed Sensing	Dec 15, 2021	compressed sensingDistributed Optimization	—Unverified
Communication-Efficient Federated Distillation	Dec 1, 2020	Federated Learningimage-classification	—Unverified
Communication-Efficient Federated Distillation with Active Data Sampling	Mar 14, 2022	Federated LearningPrivacy Preserving	—Unverified
Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems	Feb 13, 2023	Bilevel OptimizationFederated Learning	—Unverified
Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation	Mar 18, 2019	Federated Learning	—Unverified
Communication-Efficient Federated Group Distributionally Robust Optimization	Oct 8, 2024	Federated Learning	—Unverified
Communication Efficient Federated Learning over Multiple Access Channels	Jan 23, 2020	Federated LearningInformativeness	—Unverified
Communication-Efficient Federated Learning via Optimal Client Sampling	Jul 30, 2020	Federated LearningLanguage Modeling	—Unverified
Communication-Efficient Federated Learning via Quantized Compressed Sensing	Nov 30, 2021	compressed sensingFederated Learning	—Unverified
Communication-Efficient Federated Learning for Neural Machine Translation	Dec 12, 2021	Federated LearningMachine Translation	—Unverified
Communication Efficient Federated Learning for Generalized Linear Bandits	Feb 2, 2022	Federated Learningparameter estimation	—Unverified
Communication Efficient Federated Learning via Ordered ADMM in a Fully Decentralized Setting	Feb 5, 2022	Distributed OptimizationFederated Learning	—Unverified
Communication-Efficient Federated Learning Using Censored Heavy Ball Descent	Sep 24, 2022	Federated Learning	—Unverified
Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence	Feb 27, 2023	Federated LearningPrivacy Preserving	—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