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Found 7 papers, 0 papers with code
Federated Two Stage Decoupling With Adaptive Personalization Layers
While most existing clustered federated learning methods employ either model gradients or inference outputs as metrics for client partitioning, with the goal of grouping similar devices together, may still have heterogeneity within each cluster.
FEVERLESS: Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels
Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without revealing private local information.
PIVODL: Privacy-preserving vertical federated learning over distributed labels
Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data.
Federated Learning on Non-IID Data: A Survey
Federated learning is an emerging distributed machine learning framework for privacy preservation.
From Federated Learning to Federated Neural Architecture Search: A Survey
Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern.
Distributed, Parallel, and Cluster Computing
Real-time Federated Evolutionary Neural Architecture Search
Federated learning is a distributed machine learning approach to privacy preservation and two major technical challenges prevent a wider application of federated learning.
Multi-objective Evolutionary Federated Learning
A scalable method for encoding network connectivity is adapted to federated learning to enhance the efficiency in evolving deep neural networks.
