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 1251–1300 of 6771 papers

Title	Date	Tasks	Status	Hype
Why Go Full? Elevating Federated Learning Through Partial Network Updates	Oct 15, 2024	Federated Learning	CodeCode Available	1
Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep Learning	Oct 15, 2024	Federated Learning	CodeCode Available	0
WPFed: Web-based Personalized Federation for Decentralized Systems	Oct 15, 2024	Federated Learning	—Unverified	0
Feasibility Analysis of Federated Neural Networks for Explainable Detection of Atrial Fibrillation	Oct 14, 2024	Federated LearningPrivacy Preserving	—Unverified	0
A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals	Oct 14, 2024	Dimensionality ReductionFederated Learning	—Unverified	0
A few-shot Label Unlearning in Vertical Federated Learning	Oct 14, 2024	Computational EfficiencyFederated Learning	—Unverified	0
Federated Data-Efficient Instruction Tuning for Large Language Models	Oct 14, 2024	Federated Learning	—Unverified	0
Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Network	Oct 14, 2024	Federated Learning	—Unverified	0
Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models	Oct 14, 2024	Federated LearningMixture-of-Experts	CodeCode Available	1
Fed-pilot: Optimizing LoRA Allocation for Efficient Federated Fine-Tuning with Heterogeneous Clients	Oct 14, 2024	Federated Learning	—Unverified	0
FedECADO: A Dynamical System Model of Federated Learning	Oct 13, 2024	Distributed OptimizationFederated Learning	—Unverified	0
Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning	Oct 13, 2024	Federated Learning	—Unverified	0
Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid	Oct 13, 2024	Edge-computingFederated Learning	—Unverified	0
A New Perspective to Boost Performance Fairness for Medical Federated Learning	Oct 12, 2024	FairnessFederated Learning	CodeCode Available	0
Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting	Oct 12, 2024	Federated Learning	—Unverified	0
FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models	Oct 12, 2024	Arithmetic ReasoningFederated Learning	CodeCode Available	1
SoK: Verifiable Cross-Silo FL	Oct 11, 2024	Federated Learning	—Unverified	0
Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding	Oct 11, 2024	Anomaly DetectionFederated Learning	—Unverified	0
Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformation	Oct 11, 2024	Binary ClassificationFederated Learning	—Unverified	0
Federated Learning in Practice: Reflections and Projections	Oct 11, 2024	Federated Learning	—Unverified	0
Accelerated Distributed Stochastic Non-Convex Optimization over Time-Varying Directed Networks	Oct 11, 2024	Federated Learningimage-classification	—Unverified	0
Gradients Stand-in for Defending Deep Leakage in Federated Learning	Oct 11, 2024	Federated Learning	CodeCode Available	0
GAI-Enabled Explainable Personalized Federated Semi-Supervised Learning	Oct 11, 2024	Federated LearningKnowledge Distillation	—Unverified	0
Evaluating Federated Kolmogorov-Arnold Networks on Non-IID Data	Oct 11, 2024	Federated LearningKolmogorov-Arnold Networks	CodeCode Available	0
DistDD: Distributed Data Distillation Aggregation through Gradient Matching	Oct 11, 2024	Federated LearningNeural Architecture Search	—Unverified	0
Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees	Oct 11, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	0
Unlocking FedNL: Self-Contained Compute-Optimized Implementation	Oct 11, 2024	Federated LearningSecond-order methods	—Unverified	0
Efficient Adaptive Federated Optimization	Oct 10, 2024	Federated Learning	—Unverified	0
Federated Learning for Data Market: Shapley-UCB for Seller Selection and Incentives	Oct 10, 2024	Federated Learning	—Unverified	0
Randomized Asymmetric Chain of LoRA: The First Meaningful Theoretical Framework for Low-Rank Adaptation	Oct 10, 2024	Federated Learningparameter-efficient fine-tuning	—Unverified	0
RAB^2-DEF: Dynamic and explainable defense against adversarial attacks in Federated Learning to fair poor clients	Oct 10, 2024	FairnessFederated Learning	—Unverified	0
FedEP: Tailoring Attention to Heterogeneous Data Distribution with Entropy Pooling for Decentralized Federated Learning	Oct 10, 2024	Federated Learning	—Unverified	0
Scalable and Resource-Efficient Second-Order Federated Learning via Over-the-Air Aggregation	Oct 10, 2024	Federated Learning	—Unverified	0
Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning	Oct 10, 2024	Domain AdaptationFederated Learning	—Unverified	0
A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning	Oct 10, 2024	Federated Learning	—Unverified	0
Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning	Oct 9, 2024	Federated Learning	CodeCode Available	0
Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration	Oct 9, 2024	Federated Learning	—Unverified	0
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning	Oct 9, 2024	BenchmarkingFairness	CodeCode Available	0
PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning	Oct 9, 2024	FairnessFederated Learning	—Unverified	0
Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare	Oct 9, 2024	Federated Learning	—Unverified	0
OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement	Oct 9, 2024	Federated Learning	—Unverified	0
Adaptive Guidance for Local Training in Heterogeneous Federated Learning	Oct 9, 2024	Federated Learning	CodeCode Available	0
Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation	Oct 9, 2024	Contrastive LearningFederated Learning	—Unverified	0
FACMIC: Federated Adaptative CLIP Model for Medical Image Classification	Oct 8, 2024	Domain AdaptationFederated Learning	CodeCode Available	1
Privacy-Enhanced Over-the-Air Federated Learning via Client-Driven Power Balancing	Oct 8, 2024	Federated Learning	—Unverified	0
Private and Communication-Efficient Federated Learning based on Differentially Private Sketches	Oct 8, 2024	Federated Learning	—Unverified	0
De-VertiFL: A Solution for Decentralized Vertical Federated Learning	Oct 8, 2024	Federated LearningPrivacy Preserving	—Unverified	0
FedGraph: A Research Library and Benchmark for Federated Graph Learning	Oct 8, 2024	BenchmarkingFederated Learning	CodeCode Available	2
Communication-Efficient Federated Group Distributionally Robust Optimization	Oct 8, 2024	Federated Learning	—Unverified	0
Federated Learning with Dynamic Client Arrival and Departure: Convergence and Rapid Adaptation via Initial Model Construction	Oct 8, 2024	Federated Learning	—Unverified	0

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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