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A federated graph neural network framework for privacy-preserving personalization [PDF]
Nature Communications, 2021 Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation.
Chuhan Wu +4 more
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Dual Personalization on Federated Recommendation [PDF]
International Joint Conference on Artificial Intelligence, 2023 Federated recommendation is a new Internet service architecture that aims to provide privacy-preserving recommendation services in federated settings.
Chunxu Zhang +6 more
semanticscholar +1 more source
Survey of Personalization Techniques for Federated Learning [PDF]
2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), 2020 Federated learning enables machine learning models to learn from private decentralized data without compromising privacy. The standard formulation of federated learning produces one shared model for all clients.
V. Kulkarni +2 more
semanticscholar +1 more source
FedTP: Federated Learning by Transformer Personalization [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 Federated learning is an emerging learning paradigm where multiple clients collaboratively train a machine learning model in a privacy-preserving manner.
Hongxia Li +6 more
semanticscholar +1 more source
Frontiers in Digital Health, 2023 Introduction Personalization is a much-discussed approach to improve adherence and outcomes for Digital Mental Health interventions (DMHIs). Yet, major questions remain open, such as (1) what personalization is, (2) how prevalent it is in practice, and ...
Silvan Hornstein +4 more
semanticscholar +1 more source
PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization [PDF]
The Web Conference, 2021 Federated learning (FL) has become a prevalent distributed machine learning paradigm with improved privacy. After learning, the resulting federated model should be further personalized to each different client. While several methods have been proposed to
Bingyan Liu, Yao Guo, Xiangqun Chen
semanticscholar +1 more source
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning [PDF]
International Joint Conference on Artificial Intelligence, 2021 Conventional federated learning (FL) trains one global model for a federation of clients with decentralized data, reducing the privacy risk of centralized training.
Jun Luo, Shandong Wu
semanticscholar +1 more source
Multi-center federated learning: clients clustering for better personalization [PDF]
World wide web (Bussum), 2020 Personalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises.
Ming Xie +5 more
semanticscholar +1 more source
Journal of Research in Interactive Marketing, 2022 PurposeArtificial intelligence (AI) technology has revolutionized customers' interactive marketing experience. Although there have been a substantial number of studies exploring the application of AI in interactive marketing, personalization as an ...
Youjiang Gao, Hongfei Liu
semanticscholar +1 more source
World Psychiatry, 2021 The current management of patients with primary psychosis worldwide is often remarkably stereotyped. In almost all cases an antipsychotic medication is prescribed, with second‐generation antipsychotics usually preferred to first‐generation ones ...
M. Maj +17 more
semanticscholar +1 more source