Federated Learning
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Benchmarks
Cityscapes heterogeneous
Landmarks-User-160k
Most implemented
Communication-Efficient Learning of Deep Networks from Decentralized Data
Federated Optimization in Heterogeneous Networks
Adaptive Personalized Federated Learning
Advances and Open Problems in Federated Learning
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
Adaptive Federated Optimization
Papers
Structural Negative Transfer in Federated Graph Neural Networks: Diagnosis, Causal Investigation, and the Limits of Divergence-Aware Mitigation
Federated learning lets multiple participants train a shared model without pooling raw data, by exchanging locally trained model updates instead. Federated averaging assumes that averaging local models is a reasonable wa…
Federated LearningHybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning
Cross-silo Federated Learning (FL) enables geographically distributed institutions to collaboratively train machine learning models without sharing raw data. In wide-area deployments, however, communication delays often …
Federated LearningOmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis
Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms cannot analyze these data types together. Meanwhile, compliance with H…
Federated LearningA Trust-Network-Based Federated Learning Framework for Multi-Center Aging Clock Prediction
Aging clocks quantify biological aging and help characterize individual health status. What protein interactions are important for accurate aging clocks, and are they zeroth-order or higher-order? Addressing these questi…
Federated LearningBeyond Conventional Federated Learning via High-Order Regularization
Federated clients that perform several local optimization steps can return parameter displacements with widely different magnitudes. The quadratic regularization of FedProx grows linearly with displacement and therefore …
Federated LearningPrivacy-Preserving Split Learning for Federated LLM Fine-Tuning
Fine-tuning large language models (LLMs) on domain-specific data is essential for downstream adaptation. In many deployments, a participant cannot hold the complete model locally. This happens because the model owner kee…
Federated Learning