Holarchic Structures for Decentralized Deep Learning - A Performance Analysis

The Internet of Things empowers a high level of interconnectivity between smart phones, sensors and wearable devices. These technological developments provide unprecedented opportunities to rethink about the future of machine learning and artificial intelligence: Centralized computational intelligence can be often used for privacy-intrusive and discriminatory services that create'filter bubbles' and undermine citizens' autonomy by nudging [11, 27, 15]. In contrast, this paper envisions a more socially responsible design for digital society based on decentralized learning and collective intelligence formed by bottomup planetary-scale networks run by citizens [17, 16]. In this context, the structural elements of decentralized deep learning processes play a key role. The effectiveness of several classification and prediction operations often relies heavily on hyperparameter optimization [24, 46] and on the learning structure, for instance, the number of layers in a neural network, the interconnectivity of the neurons, the activation or deactivation of certain pathways i.e. dropout regularization [44], can enhance learning performance.