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 5851–5875 of 6771 papers

Title	Date	Tasks	Status
Collaboratively Learning Federated Models from Noisy Decentralized Data	Sep 3, 2024	Federated Learning	—Unverified
Collaborative Split Federated Learning with Parallel Training and Aggregation	Apr 22, 2025	Federated Learning	—Unverified
Collaborative Visual Place Recognition through Federated Learning	Apr 20, 2024	Contrastive LearningFederated Learning	—Unverified
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy	Feb 20, 2022	Federated LearningPrivacy Preserving	—Unverified
ColNet: Collaborative Optimization in Decentralized Federated Multi-task Learning Systems	Jan 17, 2025	Federated LearningMulti-Task Learning	—Unverified
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators	Jul 11, 2023	Federated Learning	—Unverified
Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions	Jul 20, 2022	Edge-computingFederated Learning	—Unverified
Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer	Jan 23, 2023	Federated Learningimage-classification	—Unverified
Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics	Jan 21, 2020	Active LearningFederated Learning	—Unverified
Combining Federated Learning and Control: A Survey	Jul 12, 2024	Decision MakingFederated Learning	—Unverified
Combining Stochastic Defenses to Resist Gradient Inversion: An Ablation Study	Aug 9, 2022	Federated LearningImage Classification	—Unverified
Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching	Sep 17, 2021	Federated Learning	—Unverified
Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"	Sep 30, 2024	Federated Learning	—Unverified
Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with Inexact Prox	Jul 8, 2022	Federated Learning	—Unverified
Communication and Energy Efficient Federated Learning using Zero-Order Optimization Technique	Sep 24, 2024	Federated LearningQuantization	—Unverified
Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding	Dec 5, 2021	Federated Learning	—Unverified
Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy	Apr 15, 2023	Federated Learning	—Unverified
Communication and Storage Efficient Federated Split Learning	Feb 11, 2023	Federated Learning	—Unverified
Communication Compression for Distributed Learning without Control Variates	Dec 5, 2024	Federated LearningPrivacy Preserving	—Unverified
Communication-Computation Efficient Secure Aggregation for Federated Learning	Dec 10, 2020	Federated LearningPrivacy Preserving	—Unverified
Communication Efficiency in Federated Learning: Achievements and Challenges	Jul 23, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
Communication Efficiency Optimization of Federated Learning for Computing and Network Convergence of 6G Networks	Nov 28, 2023	Federated Learning	—Unverified
Communication Efficient Adaptive Model-Driven Quantum Federated Learning	Jun 5, 2025	Federated Learning	—Unverified
Communication-Efficient Agnostic Federated Averaging	Apr 6, 2021	Federated LearningLanguage Modeling	—Unverified
Communication-Efficient and Drift-Robust Federated Learning via Elastic Net	Oct 6, 2022	Federated 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