Federated learning came into being with the increasing concern of privacy security, as
people's sensitive information is being exposed under the era of big data. It is an algorithm …

Graph neural network (GNN) is effective in modeling high-order interactions and has been
widely used in various personalized applications such as recommendation. However …

Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …

Abstract One-shot Federated Learning (FL) has recently emerged as a promising approach,
which allows the central server to learn a model in a single communication round. Despite …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

联邦学习是顺应大数据时代和人工智能技术发展而兴起的一种协调多个参与方共同训练模型的
机制. 它允许各个参与方将数据保留在本地, 在打破数据孤岛的同时保证参与方对数据的控制权 …

Federated learning (FL) is a feasible technique to learn personalized recommendation
models from decentralized user data. Unfortunately, federated recommender systems are …

Federated learning (FL) that enables edge devices to collaboratively learn a shared model
while keeping their training data locally has received great attention recently and can protect …

As societal concerns on data privacy recently increase, we have witnessed data silos among
multiple parties in various applications. Federated learning emerges as a new learning …

Academia and industry have developed several platforms to support the popular privacy-
preserving distributed learning method—federated learning (FL). However, these platforms …