KDD

2024

• 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.

2023

• 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.

2022

• 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.