Federated Graph Learning with Structure Proxy Alignment.
HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning.
Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination.
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models.
CASA: Clustered Federated Learning with Asynchronous Clients.
BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning.
FLAIM: AIM-based Synthetic Data Generation in the Federated Setting.
FedNLR: Federated Learning with Neuron-wise Learning Rates.
FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning.
FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model.
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation.
Preventing Strategic Behaviors in Collaborative Inference for Vertical Federated Learning.
PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection.
FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation.
Personalized Federated Continual Learning via Multi-Granularity Prompt.
GPFedRec: Graph-Guided Personalization for Federated Recommendation.
Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning.
Asynchronous Vertical Federated Learning for Kernelized AUC Maximization.
VertiMRF: Differentially Private Vertical Federated Data Synthesis.
FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs.
Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization.
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning.
FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction.
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning.
Privacy-Preserving Federated Learning using Flower Framework.
FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics.
Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation.
FedDefender: Client-Side Attack-Tolerant Federated Learning.
FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity.
FedPseudo: Privacy-Preserving Pseudo Value-Based Deep Learning Models for Federated Survival Analysis.
ShapleyFL: Robust Federated Learning Based on Shapley Value.
Federated Few-shot Learning.
Theoretical Convergence Guaranteed Resource-Adaptive Federated Learning with Mixed Heterogeneity.
Personalized Federated Learning with Parameter Propagation.
Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining.
CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning.
FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework.
FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy.
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework.
DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization.
FS-REAL: Towards Real-World Cross-Device Federated Learning.
FedMultimodal: A Benchmark for Multimodal Federated Learning.
PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation.
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks.
UA-FedRec: Untargeted Attack on Federated News Recommendation.
International Workshop on Federated Learning for Distributed Data Mining.
Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Data.
FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks.
Collaboration Equilibrium in Federated Learning.
Connecting Low-Loss Subspace for Personalized Federated Learning.
Communication-Efficient Robust Federated Learning with Noisy Labels.
FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy.
FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients.
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices.
Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch.
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning.
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling.
Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks.
A Practical Introduction to Federated Learning.
Last updated 6 months ago