main webpage W Topic Federated Learning arXiv (Cornell University) Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks 2023 Federated systems enable collaborative training on highly heterogeneous data through model personalization, which can be facilitated by employing multi-task learning algorithms. However, significant variation in device computing capabilities may result in sub… Article Open Federated Learning Open Article Page

Federated Learning

Decentralized machine learning Federated learning (also known as collaborative learning ) is a machine learning technique in a setting where multiple entities (often called clients) collaboratively train a model while keeping their data decentralized, rather than centrally stored. A defining characteristic of federated learning is data heterogeneity. Because client data is decentralized, data samples held by each client may not be independently and identically distributed. Federated learning is generally concerned with and motivated by issues such as data privacy, data minimization, and data access rights. Its applications involve a variety of research areas including defence, telecommunications, the Internet of things, and pharmaceuticals. … Critical Symbolic Virtual Narrative Open Federated Learning Open Article Page

Exploring foci of: arXiv (Cornell University) Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks 2023 Federated systems enable collaborative training on highly heterogeneous data through model personalization, which can be facilitated by employing multi-task learning algorithms. However, significant variation in device computing capabilities may result in substantial degradation in the convergence rate of training. To accelerate the learning procedure for diverse participants in a multi-task federated setting, more efficient and robust methods need to be developed. In this paper, we design an efficient iterative d… Open Article Page

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Explore Federated Learning W Topic Federated Learning arXiv (Cornell University) Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks 2023 Federated systems enable collaborative training on highly heterogeneous data through model personalization, which can be facilitated by employing multi-task learning algorithms. However, significant variation in device computing capabilities may result in sub… Article Critical Symbolic Virtual Narrative Open Federated Learning Open Article Page