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David Naseh, Mahdi Abdollahpour and Daniele Tarchi
This paper explores the practical implementation and performance analysis of distributed learning (DL) frameworks on various client platforms, responding to the dynamic landscape of 6G technology and the pressing need for a fully connected distributed in...
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Lorenzo Ridolfi, David Naseh, Swapnil Sadashiv Shinde and Daniele Tarchi
With the advent of 6G technology, the proliferation of interconnected devices necessitates a robust, fully connected intelligence network. Federated Learning (FL) stands as a key distributed learning technique, showing promise in recent advancements. How...
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Afsana Khan, Marijn ten Thij and Anna Wilbik
Federated learning (FL) is a privacy-preserving distributed learning approach that allows multiple parties to jointly build machine learning models without disclosing sensitive data. Although FL has solved the problem of collaboration without compromisin...
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Mehreen Tahir and Muhammad Intizar Ali
Federated Learning (FL) is a state-of-the-art technique used to build machine learning (ML) models based on distributed data sets. It enables In-Edge AI, preserves data locality, protects user data, and allows ownership. These characteristics of FL make ...
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Abdallah Moubayed, Abdallah Shami and Anwer Al-Dulaimi
The digital transformation of businesses and services is currently in full force, opening the world to a new set of unique challenges and opportunities. In this context, 6G promises to be the set of technologies, architectures, and paradigms that will pr...
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Shaoyong Li, Liang Lv, Xiaoya Li and Zhaoyun Ding
At present, most mobile App start-up prediction algorithms are only trained and predicted based on single-user data. They cannot integrate the data of all users to mine the correlation between users, and cannot alleviate the cold start problem of new use...
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