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 5751–5800 of 6771 papers

Title	Date	Tasks	Status
Caesar: A Low-deviation Compression Approach for Efficient Federated Learning	Dec 28, 2024	Federated Learning	—Unverified
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers	Nov 6, 2023	Federated Learning	—Unverified
CAFE: Catastrophic Data Leakage in Federated Learning	Jan 1, 2021	Federated Learning	—Unverified
CAFe: Cost and Age aware Federated Learning	May 24, 2024	Federated Learning	—Unverified
Cali3F: Calibrated Fast Fair Federated Recommendation System	May 26, 2022	FairnessFederated Learning	—Unverified
Can collaborative learning be private, robust and scalable?	May 5, 2022	Adversarial RobustnessFederated Learning	—Unverified
Can Decentralized Learning be more robust than Federated Learning?	Mar 7, 2023	Federated Learning	—Unverified
Can Fair Federated Learning reduce the need for Personalisation?	May 4, 2023	Federated Learning	—Unverified
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression	Jul 20, 2021	Distributed OptimizationFederated Learning	—Unverified
Canoe : A System for Collaborative Learning for Neural Nets	Aug 27, 2021	Edge-computingFederated Learning	—Unverified
Can Public Large Language Models Help Private Cross-device Federated Learning?	May 20, 2023	Federated Learning	—Unverified
Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning?	Sep 5, 2024	Federated Learning	—Unverified
Can We Trust the Similarity Measurement in Federated Learning?	Oct 20, 2023	Federated LearningModel Poisoning	—Unverified
Can You Really Backdoor Federated Learning?	Nov 18, 2019	Federated Learning	—Unverified
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model	Feb 16, 2022	Federated LearningLanguage Modeling	—Unverified
CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model	Mar 10, 2025	Federated LearningLanguage Modeling	—Unverified
Caring Without Sharing: A Federated Learning Crowdsensing Framework for Diversifying Representation of Cities	Jan 20, 2022	Federated LearningPrivacy Preserving	—Unverified
CatFedAvg: Optimising Communication-efficiency and Classification Accuracy in Federated Learning	Nov 14, 2020	ClassificationFederated Learning	—Unverified
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications	Feb 1, 2023	Data PoisoningDecoder	—Unverified
CC-FedAvg: Computationally Customized Federated Averaging	Dec 28, 2022	Federated Learning	—Unverified
CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imaging	Jul 16, 2024	DiversityFederated Learning	—Unverified
CD^2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning	Apr 8, 2022	Federated LearningMedical Image Analysis	—Unverified
CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning	Jan 1, 2022	Federated LearningMedical Image Analysis	—Unverified
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering	Jul 17, 2024	Activity RecognitionClustering	—Unverified
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning	Apr 4, 2022	Federated LearningKnowledge Distillation	—Unverified
EcoLearn: Optimizing the Carbon Footprint of Federated Learning	Oct 27, 2023	Federated Learning	—Unverified
CELEST: Federated Learning for Globally Coordinated Threat Detection	May 23, 2022	Active LearningFederated Learning	—Unverified
CELLM: An Efficient Communication in Large Language Models Training for Federated Learning	Jul 30, 2024	Federated Learning	—Unverified
Celtibero: Robust Layered Aggregation for Federated Learning	Aug 26, 2024	Federated Learning	—Unverified
Centroid Approximation for Byzantine-Tolerant Federated Learning	Jun 18, 2025	Distributed ComputingFederated Learning	—Unverified
Cerberus: Exploring Federated Prediction of Security Events	Sep 7, 2022	Federated LearningPrediction	—Unverified
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing	Mar 30, 2021	Federated Learning	—Unverified
Certified Federated Adversarial Training	Dec 20, 2021	Adversarial RobustnessFederated Learning	—Unverified
Certified Robustness for Free in Differentially Private Federated Learning	Sep 29, 2021	Federated Learning	—Unverified
CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning	Jun 14, 2021	Federated Learning	—Unverified
CFLIT: Coexisting Federated Learning and Information Transfer	Jul 26, 2022	Federated Learning	—Unverified
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models	May 22, 2024	DecoderFederated Learning	—Unverified
Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learning	Dec 29, 2021	Federated Learning	—Unverified
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images	Jan 26, 2022	Active LearningBIG-bench Machine Learning	—Unverified
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning	May 26, 2023	Federated LearningPrivacy Preserving	—Unverified
Channel-driven Decentralized Bayesian Federated Learning for Trustworthy Decision Making in D2D Networks	Oct 19, 2022	Decision MakingFederated Learning	—Unverified
Characterization of the Global Bias Problem in Aerial Federated Learning	Dec 29, 2022	Federated Learning	—Unverified
Chisme: Fully Decentralized Differentiated Deep Learning for Edge Intelligence	May 14, 2025	Deep LearningFederated Learning	—Unverified
Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning	Feb 23, 2024	Federated Learning	—Unverified
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX	May 4, 2021	Federated Learning	—Unverified
Client-Centric Federated Adaptive Optimization	Jan 17, 2025	Federated Learning	—Unverified
Client Contribution Normalization for Enhanced Federated Learning	Nov 10, 2024	Federated Learning	—Unverified
Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning	Nov 3, 2023	Federated LearningProblem Decomposition	—Unverified
Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective	Apr 9, 2020	Federated LearningStochastic Optimization	—Unverified
Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks	Aug 16, 2021	Federated LearningScheduling	—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