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pro vyhledávání: '"I.2.11"'
Consider a collection of data generators which could represent, e.g., humans equipped with a smart-phone or wearables. We want to train a personalized (or tailored) model for each data generator even if they provide only small local datasets. The ava
Externí odkaz:
http://arxiv.org/abs/2409.02064
Autor:
Bohorquez, Gonzalo, Cartlidge, John
We propose that a tree-like hierarchical structure represents a simple and effective way to model the emergent behaviour of financial markets, especially markets where there exists a pronounced intersection between social media influences and investo
Externí odkaz:
http://arxiv.org/abs/2409.00742
Multi-agent Reinforcement Learning (MARL) is emerging as a key framework for various sequential decision-making and control tasks. Unlike their single-agent counterparts, multi-agent systems necessitate successful cooperation among the agents. The de
Externí odkaz:
http://arxiv.org/abs/2408.06503
In multi-agent cooperative tasks, the presence of heterogeneous agents is familiar. Compared to cooperation among homogeneous agents, collaboration requires considering the best-suited sub-tasks for each agent. However, the operation of multi-agent s
Externí odkaz:
http://arxiv.org/abs/2408.07098
The training phase of deep neural networks requires substantial resources and as such is often performed on cloud servers. However, this raises privacy concerns when the training dataset contains sensitive content, e.g., face images. In this work, we
Externí odkaz:
http://arxiv.org/abs/2408.05092
Publikováno v:
Expert Systems with Applications, Volume 255, 2024, Article 124742
Random forests are considered a cornerstone in machine learning for their robustness and versatility. Despite these strengths, their conventional centralized training is ill-suited for the modern landscape of data that is often distributed, sensitive
Externí odkaz:
http://arxiv.org/abs/2407.19193
Autor:
Schwanck, Felipe Machado, Leipnitz, Marcos Tomazzoli, Carbonera, Joel Luís, Wickboldt, Juliano Araujo
Federated Learning (FL) is a machine learning paradigm in which many clients cooperatively train a single centralized model while keeping their data private and decentralized. FL is commonly used in edge computing, which involves placing computer wor
Externí odkaz:
http://arxiv.org/abs/2407.12980
Autor:
Gupta, Sunny, Sethi, Amit
Federated Learning (FL) offers a privacy-preserving approach to train models on decentralized data. Its potential in healthcare is significant, but challenges arise due to cross-client variations in medical image data, exacerbated by limited annotati
Externí odkaz:
http://arxiv.org/abs/2407.11652
Autor:
Zhang, Yintong, Yoder, Jason A.
Recently, Cartesian Genetic Programming has been used to evolve developmental programs to guide the formation of artificial neural networks (ANNs). This approach has demonstrated success in enabling ANNs to perform multiple tasks while avoiding catas
Externí odkaz:
http://arxiv.org/abs/2407.10359
To reduce the latency of Backpressure (BP) routing in wireless multi-hop networks, we propose to enhance the existing shortest path-biased BP (SP-BP) and sojourn time-based backlog metrics, since they introduce no additional time step-wise signaling
Externí odkaz:
http://arxiv.org/abs/2407.09753