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pro vyhledávání: '"A. Chaouki"'
In this paper, we introduce a method for fine-tuning Large Language Models (LLMs), inspired by Multi-Task learning in a federated manner. Our approach leverages the structure of each client's model and enables a learning scheme that considers other c
Externí odkaz:
http://arxiv.org/abs/2410.15524
Second-order federated learning (FL) algorithms offer faster convergence than their first-order counterparts by leveraging curvature information. However, they are hindered by high computational and storage costs, particularly for large-scale models.
Externí odkaz:
http://arxiv.org/abs/2410.07662
Model-free algorithms are brought into the control system's research with the emergence of reinforcement learning algorithms. However, there are two practical challenges of reinforcement learning-based methods. First, learning by interacting with the
Externí odkaz:
http://arxiv.org/abs/2409.10722
Federated Learning (FL) offers a promising approach for collaborative machine learning across distributed devices. However, its adoption is hindered by the complexity of building reliable communication architectures and the need for expertise in both
Externí odkaz:
http://arxiv.org/abs/2408.13010
Akademický článek
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Autor:
Elbakary, Ahmed, Issaid, Chaouki Ben, Shehab, Mohammad, Seddik, Karim, ElBatt, Tamer, Bennis, Mehdi
Federated learning is a machine learning approach where multiple devices collaboratively learn with the help of a parameter server by sharing only their local updates. While gradient-based optimization techniques are widely adopted in this domain, th
Externí odkaz:
http://arxiv.org/abs/2406.06655
Decision Tree (DT) Learning is a fundamental problem in Interpretable Machine Learning, yet it poses a formidable optimisation challenge. Despite numerous efforts dating back to the early 1990's, practical algorithms have only recently emerged, prima
Externí odkaz:
http://arxiv.org/abs/2406.02175
The Oceanus Procellarum region, characterized by its vast basaltic plains and pronounced volcanic activity, serves as a focal point for understanding the volcanic history of the Moon. Leveraging the Gravity Recovery and Interior Laboratory (GRAIL) mi
Externí odkaz:
http://arxiv.org/abs/2405.07639
As the amount and complexity of data grows, retrieving it has become a more difficult task that requires greater knowledge and resources. This is especially true for the logistics industry, where new technologies for data collection provide tremendou
Externí odkaz:
http://arxiv.org/abs/2405.15792
Decision Trees are prominent prediction models for interpretable Machine Learning. They have been thoroughly researched, mostly in the batch setting with a fixed labelled dataset, leading to popular algorithms such as C4.5, ID3 and CART. Unfortunatel
Externí odkaz:
http://arxiv.org/abs/2404.06403