Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Nazemi, Niousha"'
Autor:
Nazemi, Niousha, Tavallaie, Omid, Chen, Shuaijun, Mandalari, Anna Maria, Thilakarathna, Kanchana, Holz, Ralph, Haddadi, Hamed, Zomaya, Albert Y.
Federated Learning (FL) is a promising distributed learning framework designed for privacy-aware applications. FL trains models on client devices without sharing the client's data and generates a global model on a server by aggregating model updates.
Externí odkaz:
http://arxiv.org/abs/2409.01722
Autor:
Nazemi, Niousha, Tavallaie, Omid, Mandalari, Anna Maria, Haddadi, Hamed, Holz, Ralph, Zomaya, Albert Y.
This paper investigates the impact of internet centralization on DNS provisioning, particularly its effects on vulnerable populations such as the indigenous people of Australia. We analyze the DNS dependencies of Australian government domains that se
Externí odkaz:
http://arxiv.org/abs/2408.12958
Federated Learning (FL) is a promising privacy-aware distributed learning framework that can be deployed on various devices, such as mobile phones, desktops, and devices equipped with CPUs or GPUs. In the context of server-based Federated Learning as
Externí odkaz:
http://arxiv.org/abs/2408.08699
Federated Learning (FL) is a decentralized machine learning approach where client devices train models locally and send them to a server that performs aggregation to generate a global model. FL is vulnerable to model inversion attacks, where the serv
Externí odkaz:
http://arxiv.org/abs/2405.01144