Structure plays a key role in learning performance. In centralized computational systems, hyperparameter optimization and regularization techniques such as dropout are computational means to enhance learning performance by adjusting the deep hierarchical structure. However, in decentralized deep learning by the Internet of Things, the structure is an actual network of autonomous interconnected devices such as smart phones that interact via complex network protocols. Self-adaptation of the learning structure is a challenge. Uncertainties such as network latency, node and link failures or even bottlenecks by limited processing capacity and energy availability can significantly downgrade learning performance. Network self-organization and self-management is complex, while it requires additional computational and network resources that hinder the feasibility of decentralized deep learning. In contrast, this paper introduces a self-adaptive learning approach based on holarchic learning structures for exploring, mitigating and boosting learning performance in distributed environments with uncertainties. A large-scale performance analysis with 864,000 experiments fed with synthetic and real-world data from smart grid and smart city pilot projects confirm the cost-effectiveness of holarchic structures for decentralized deep learning.
Multi-Level Location Problems are generally considered as complex. To deal with these problems we propose an approach based on Holonic MultiAgent Systems (HMAS). HMAS have already proven to be a convenient way to engineer complex systems. This approach was merged with Artificial Potential Fields (APF) mechanims. The solution is obtained simultaneously with the holarchy. The holarchy is thus used to exploit and control the emergence of the solution. This solution is then evaluated to check its relevance according to global objectives represented thanks to a fitness function. This model was efficiently applied to a multi-level distribution system.